non directional two tailed hypothesis

Upper-tailed, Lower-tailed, Two-tailed Tests

The research or alternative hypothesis can take one of three forms. An investigator might believe that the parameter has increased, decreased or changed. For example, an investigator might hypothesize:  

The exact form of the research hypothesis depends on the investigator's belief about the parameter of interest and whether it has possibly increased, decreased or is different from the null value. The research hypothesis is set up by the investigator before any data are collected.

Rejection Region for Upper-Tailed Z Test (H : μ > μ ) with α=0.05

The decision rule is: Reject H if Z 1.645.

Rejection Region for Lower-Tailed Z Test (H 1 : μ < μ 0 ) with α =0.05

The decision rule is: Reject H 0 if Z < 1.645.

Rejection Region for Two-Tailed Z Test (H 1 : μ ≠ μ 0 ) with α =0.05

The decision rule is: Reject H 0 if Z < -1.960 or if Z > 1.960.

The complete table of critical values of Z for upper, lower and two-tailed tests can be found in the table of Z values to the right in "Other Resources."

Critical values of t for upper, lower and two-tailed tests can be found in the table of t values in "Other Resources."

• Step 4. Compute the test statistic.  

Here we compute the test statistic by substituting the observed sample data into the test statistic identified in Step 2.

• Step 5. Conclusion.  

The final conclusion is made by comparing the test statistic (which is a summary of the information observed in the sample) to the decision rule. The final conclusion will be either to reject the null hypothesis (because the sample data are very unlikely if the null hypothesis is true) or not to reject the null hypothesis (because the sample data are not very unlikely).  

If the null hypothesis is rejected, then an exact significance level is computed to describe the likelihood of observing the sample data assuming that the null hypothesis is true. The exact level of significance is called the p-value and it will be less than the chosen level of significance if we reject H 0 .

Statistical computing packages provide exact p-values as part of their standard output for hypothesis tests. In fact, when using a statistical computing package, the steps outlined about can be abbreviated. The hypotheses (step 1) should always be set up in advance of any analysis and the significance criterion should also be determined (e.g., α =0.05). Statistical computing packages will produce the test statistic (usually reporting the test statistic as t) and a p-value. The investigator can then determine statistical significance using the following: If p < α then reject H 0 .  

• Step 1. Set up hypotheses and determine level of significance

H 0 : μ = 191 H 1 : μ > 191                 α =0.05

The research hypothesis is that weights have increased, and therefore an upper tailed test is used.

• Step 2. Select the appropriate test statistic.

Because the sample size is large (n > 30) the appropriate test statistic is

• Step 3. Set up decision rule.  

In this example, we are performing an upper tailed test (H 1 : μ> 191), with a Z test statistic and selected α =0.05.   Reject H 0 if Z > 1.645.

We now substitute the sample data into the formula for the test statistic identified in Step 2.  

We reject H 0 because 2.38 > 1.645. We have statistically significant evidence at a =0.05, to show that the mean weight in men in 2006 is more than 191 pounds. Because we rejected the null hypothesis, we now approximate the p-value which is the likelihood of observing the sample data if the null hypothesis is true. An alternative definition of the p-value is the smallest level of significance where we can still reject H 0 . In this example, we observed Z=2.38 and for α=0.05, the critical value was 1.645. Because 2.38 exceeded 1.645 we rejected H 0 . In our conclusion we reported a statistically significant increase in mean weight at a 5% level of significance. Using the table of critical values for upper tailed tests, we can approximate the p-value. If we select α=0.025, the critical value is 1.96, and we still reject H 0 because 2.38 > 1.960. If we select α=0.010 the critical value is 2.326, and we still reject H 0 because 2.38 > 2.326. However, if we select α=0.005, the critical value is 2.576, and we cannot reject H 0 because 2.38 < 2.576. Therefore, the smallest α where we still reject H 0 is 0.010. This is the p-value. A statistical computing package would produce a more precise p-value which would be in between 0.005 and 0.010. Here we are approximating the p-value and would report p < 0.010.                  

In all tests of hypothesis, there are two types of errors that can be committed. The first is called a Type I error and refers to the situation where we incorrectly reject H 0 when in fact it is true. This is also called a false positive result (as we incorrectly conclude that the research hypothesis is true when in fact it is not). When we run a test of hypothesis and decide to reject H 0 (e.g., because the test statistic exceeds the critical value in an upper tailed test) then either we make a correct decision because the research hypothesis is true or we commit a Type I error. The different conclusions are summarized in the table below. Note that we will never know whether the null hypothesis is really true or false (i.e., we will never know which row of the following table reflects reality).

Table - Conclusions in Test of Hypothesis

In the first step of the hypothesis test, we select a level of significance, α, and α= P(Type I error). Because we purposely select a small value for α, we control the probability of committing a Type I error. For example, if we select α=0.05, and our test tells us to reject H 0 , then there is a 5% probability that we commit a Type I error. Most investigators are very comfortable with this and are confident when rejecting H 0 that the research hypothesis is true (as it is the more likely scenario when we reject H 0 ).

When we run a test of hypothesis and decide not to reject H 0 (e.g., because the test statistic is below the critical value in an upper tailed test) then either we make a correct decision because the null hypothesis is true or we commit a Type II error. Beta (β) represents the probability of a Type II error and is defined as follows: β=P(Type II error) = P(Do not Reject H 0 | H 0 is false). Unfortunately, we cannot choose β to be small (e.g., 0.05) to control the probability of committing a Type II error because β depends on several factors including the sample size, α, and the research hypothesis. When we do not reject H 0 , it may be very likely that we are committing a Type II error (i.e., failing to reject H 0 when in fact it is false). Therefore, when tests are run and the null hypothesis is not rejected we often make a weak concluding statement allowing for the possibility that we might be committing a Type II error. If we do not reject H 0 , we conclude that we do not have significant evidence to show that H 1 is true. We do not conclude that H 0 is true.

 The most common reason for a Type II error is a small sample size.

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Content ©2017. All Rights Reserved. Date last modified: November 6, 2017. Wayne W. LaMorte, MD, PhD, MPH

Directional vs. Non-Directional Hypothesis in Research

In the world of research and statistical analysis, formulating hypotheses is a crucial step in the scientific process. Hypotheses guide researchers in making predictions and testing relationships between variables. When it comes to hypotheses, there are two main types: directional and non-directional.

Directional Hypothesis

A directional hypothesis, also known as a one-tailed hypothesis, is formulated with a specific predicted direction of the relationship between variables. It indicates an expectation of the relationship being either positive or negative.

Example of Directional Hypothesis

Advantages of directional hypothesis, non-directional hypothesis.

Non-directional hypotheses are often used when there is insufficient prior knowledge or theoretical basis to predict the direction of the relationship. It allows for a more exploratory approach, where the researcher is open to discovering the nature of the relationship through data analysis .

Example of Non-Directional Hypothesis

Read More: Population vs Sample | Examples

Advantages of Non-Directional Hypothesis:

Difference between directional and non-directional hypotheses.

Choosing Between Directional and Non-Directional Hypotheses: The choice between a directional and non-directional hypothesis depends on the research question, existing knowledge, and theoretical background. Here are a few considerations for selecting the appropriate type of hypothesis:

Directional vs. Non-Directional Hypothesis

Directional hypotheses offer specific predictions about the expected direction of the relationship, whereas non-directional hypotheses allow for more exploratory investigations without preconceived notions of the direction.

Remember, hypotheses serve as a roadmap for research, and regardless of their type, they play a crucial role in scientific inquiry and the pursuit of knowledge.

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Hypothesis ( AQA A Level Psychology )

Revision note.

Psychology Content Creator

• A hypothesis is a testable statement written as a prediction of what the researcher expects to find as a result of their experiment
• A hypothesis should be no more than one sentence long
• The hypothesis needs to include the independent variable (IV) and the dependent variable (DV)
• For example - stating that you will measure ‘aggression’ is not enough ('aggression' has not been operationalised)
• by exposing some children to an aggressive adult model whilst other children are not exposed to an aggressive adult model (operationalisation of the IV) 
• number of imitative and non-imitative acts of aggression performed by the child (operationalisation of the DV)

The Experimental Hypothesis

• Children who are exposed to an aggressive adult model will perform more acts of imitative and non-imitative aggression than children who have not been exposed to an aggressive adult model
• The experimental hypothesis can be written as a  directional hypothesis or as a non-directional hypothesis

The Experimental Hypothesis: Directional 

• A directional experimental hypothesis (also known as one-tailed)  predicts the direction of the change/difference (it anticipates more specifically what might happen)
• A directional hypothesis is usually used when there is previous research which support a particular theory or outcome i.e. what a researcher might expect to happen
• Participants who drink 200ml of an energy drink 5 minutes before running 100m will be faster (in seconds) than participants who drink 200ml of water 5 minutes before running 100m
• Participants who learn a poem in a room in which loud music is playing will recall less of the poem's content than participants who learn the same poem in a silent room

 The Experimental Hypothesis: Non-Directional 

• A non-directional experimental hypothesis (also known as two -tailed) does not predict the direction of the change/difference (it is an 'open goal' i.e. anything could happen)
• A non-directional hypothesis is usually used when there is either no or little previous research which support a particular theory or outcome i.e. what the researcher cannot be confident as to what will happen
• There will be a difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running 
• There will be a difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room

The Null Hypothesis

• All published psychology research must include the null hypothesis
• There will be no difference in children's acts of imitative and non-imitative aggression depending on whether they have observed an aggressive adult model or a non-aggressive adult model
• The null hypothesis has to begin with the idea that the IV will have no effect on the DV  because until the experiment is run and the results are analysed it is impossible to state anything else! 
• To put this in 'laymen's terms: if you bought a lottery ticket you could not predict that you are going to win the jackpot: you have to wait for the results to find out (spoiler alert: the chances of this happening are soooo low that you might as well save your cash!)
• There will be no difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running 
• There will be no difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room
• (NB this is not quite so slick and easy with a directional hypothesis as this sort of hypothesis will never begin with 'There will be a difference')
• this is why the null hypothesis is so important - it tells the researcher whether or not their experiment has shown a difference in conditions (which is generally what they want to see, otherwise it's back to the drawing board...)

Worked example

Jim wants to test the theory that chocolate helps your ability to solve word-search puzzles

He believes that sugar helps memory as he has read some research on this in a text book

He puts up a poster in his sixth-form common room asking for people to take part after school one day and explains that they will be required to play two memory games, where eating chocolate will be involved

(a)  Should Jim use a directional hypothesis in this study? Explain your answer (2 marks)

(b)  Write a suitable hypothesis for this study. (4 marks)

a) Jim should use a directional hypothesis (1 mark)

    because previous research exists that states what might happen (2 nd mark)

b)  'Participants will remember more items from a shopping list in a memory game within the hour after eating 50g of chocolate, compared to when they have not consumed any chocolate'

• 1 st mark for directional
• 2 nd mark for IV- eating chocolate
• 3 rd mark for DV- number of items remembered
• 4 th mark for operationalising both IV & DV
• If you write a non-directional or null hypothesis the mark is 0
• If you do not get the direction correct the mark is zero
• Remember to operationalise the IV & DV

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Author: Claire Neeson

Claire has been teaching for 34 years, in the UK and overseas. She has taught GCSE, A-level and IB Psychology which has been a lot of fun and extremely exhausting! Claire is now a freelance Psychology teacher and content creator, producing textbooks, revision notes and (hopefully) exciting and interactive teaching materials for use in the classroom and for exam prep. Her passion (apart from Psychology of course) is roller skating and when she is not working (or watching 'Coronation Street') she can be found busting some impressive moves on her local roller rink.

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Non-Directional Hypothesis

A non-directional hypothesis is a two-tailed hypothesis that does not predict the direction of the difference or relationship (e.g. girls and boys are different in terms of helpfulness).

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One-tailed and Two-tailed Tests

by Karen Grace-Martin   30 Comments

I was recently asked about when to use one and two tailed tests.

The long answer is:  Use one tailed tests when you have a specific hypothesis about the direction of your relationship.  Some examples include you hypothesize that one group mean is larger than the other; you hypothesize that the correlation is positive; you hypothesize that the proportion is below .5.

The short answer is: Never use one tailed tests.

1. Only a few statistical tests even can have one tail: z tests and t tests.  So you’re severely limited.  F tests, Chi-square tests , etc. can’t accommodate one-tailed tests because their distributions are not symmetric.  Most statistical methods, such as regression and ANOVA , are based on these tests, so you will rarely have the chance to implement them.

2. Probably because they are rare, reviewers balk at one-tailed tests.  They tend to assume that you are trying to artificially boost the power of your test.  Theoretically, however, there is nothing wrong with them when the hypothesis and the statistical test are right for them.

Reader Interactions

June 10, 2022 at 5:48 am

Dear Karen,

I am running a regression analysis, I wonder if the significance in table output for ANOVA is inherently one-tailed or two-tailed? I have a directional hypothesis, so I am wondering if I still have to divide the significance value? Thank you in advance!

June 15, 2022 at 11:08 am

It’s two-tailed. And the F tests cannot be made directional.

October 9, 2019 at 7:46 pm

Say for example I am testing a hypothesis at .05 and it is a one tailed test. I expect the treatment to have a decrease and the cut off for significance is t = -1.67 so this is a left tail test. If t value after I run the test is +2.24 is this non significant since it is not in the hypothesized direction (negative)?

August 30, 2019 at 2:13 am

does a one-tailed test always require a one-sided (directional) hypothesis, and does a two-tailed test always require a two-sided (nondirectional) hypothesis?

Asked in the other direction: Does a one-sided (directional) hypothesis always require a one-sided test, and does a two-sided (nondirectional) hypothesis always require a two-sided test?

Thanks for clarifying Claudia

September 3, 2019 at 10:11 am

Hi Claudia,

Good question. So technically, when I am talking about one-tailed test, I do mean a test of a directional hypothesis. Likewise, I mean a non-directional hypothesis when I say two-tailed test.

In z-tests and t-tests, which are symmetric distributions, these terms are indeed interchangeable. But it definitely gets more complicated once you start talking about Chi-square tests and F-tests. Both of these have 0 at the left end and all high values of the statistic in one tail. So you can’t distinguish directions for these kinds of test statistics. So you can only test nondirectional hypotheses.

May 1, 2019 at 2:30 am

I have seen textbooks reporting confidence intervals of a standard deviation using low and high values of the chi-square statistic. Is it not based on a two-sided test?

August 28, 2018 at 7:14 am

There is no need to have a specific directional hypothesis (although you would usually have one), all that is needed to justify is to have a directional claim. Most claims in published scientific research and applied research are directional, since the moment you say the difference is positive or negative you have a directional claim. The only way to avoid it is to not mention the observed difference, or to state the difference as “plus or minus X” which would be ridiculous in most contexts.

Also, you should not mistake the tailed-ness of a statistical distribution with the tailed-ness of a hypothesis. While the Chi-Square or F-distribution might only have one tail, they can still be used for inference of one-sided and two-sided hypothesis alike. One can go as far back as Fisher and find examples of that (Statistical Methods for Research Workers).

If you are interested in more detailed arguments for the use of one-sided tests see the series of articles on The One-Sided Project website at https://www.onesided.org/ .

March 2, 2018 at 1:07 am

Hello, Good Morning to all, I am working on a project of Behavior Based Safety thesis work in which I have to assess data by SPSS software. Can any one could help me because i haven’t any idea about this software

May 17, 2018 at 10:06 am

Hi Muhammad, We have a number of free resources on SPSS, but if you’re in the middle of a project we have an on-demand tutorial that will get you not just started, but able to use it: http://theanalysisinstitute.com/introspss/

August 24, 2016 at 12:08 pm

Thank you for the discussions about the power for one-sided test. I agree that we should be careful when we decide to use a one-sided test. For Chi-square test when comparing two proportions, we can use two approaches: normal-theory method (the z-test) and contingency-table approach (the Chi-square test). For the normal-theory test, it requires a large sample size with n>5 or n*proportion >10. If your proposed study satisfied this requirement, we can use normal-theory method which is z-test to compute the power for the one-sided test. If you are interested in how to compute the one-sided test power, you can refer to the textbook by Marcello Pagano and Kimberlee Gauvreau’s 2nd edition titled “Principles of Biostatistics” section 14.5 on page 330 regarding sample size estimation for one-sided hypothesis test for proportions. Thanks.

September 1, 2015 at 2:30 am

Chi-squared tests are ALWAYS one-tailed… You only reject the null hypothesis when the test statistic falls into the right tail of the chi-square distribution.

What chi-squared tests are generally NOT is “directional”. They generally do not test whether the observed values are greater than or smaller than expected, only that they differ significantly.

January 26, 2016 at 10:57 pm

If “Chi-squared tests are ALWAYS one-tailed…”, why is Karen saying in her above statement that “F tests, Chi-square tests, etc. can’t accommodate one-tailed tests”?

Sorry, but I am bit confused. Can you help?

January 27, 2016 at 11:30 am

Hi Dippies,

Perhaps better wording is “F tests, Chi-square tests, etc. can’t accommodate directional tests.” Because there is only one tail for these distributions in which to find significance, it can’t distinguish between non-directional tests (eg, H1: mu1 – mu2 not equal to 0) and directional tests (eg, H1: mu1-mu2 greater than 0). In a t-test or z-test, we can either split alpha between two tails for a non-directional test or put alpha all into one tail for a directional test. We can then see whether we’re in the right tail based on the sign of the test statistic. F’s and Chi-sq values are squared. So they’re always positive. You get the same F value regardless of the direction of the means.

October 19, 2017 at 10:29 am

Thanks for the explanation… I am wondering: what happens if I have two methods, m1 and m2, and I want to show that m1 performs better (e.g. gives a higher value) than m2? Should I still use a two-tailed test? How can I show that m1 is, in fact, better (and not just different) than m2 (assuming the test proves significant)? Thanks for any comments.

July 22, 2015 at 6:14 pm

My hypothesis says there is a positive relation between religiosity and altruistic behavior. Would a two-tailed approach using Pearson Correlation do the trick?

Some very helpful information available here! Thank you.

July 1, 2015 at 2:50 am

Hi Karen, I run paired sample t-test and it just has P-two tailed. So how to convert P-two tailed to t critical one tailed. I have t critical two tailed and df already. Thanks so much

March 20, 2015 at 2:41 pm

I have used a one-tailed test but the effect went into the opposite direction. How do I have to calculate my p-Value now. Thanks for your reply, Mirco

February 3, 2015 at 10:24 pm

F-tests are almost always one-tailed. You would convert a two-tailed test’s p-value into a one-tailed test’s p-value by *halving* the p-value, not multiplying by 2 (as recommended above).

February 6, 2015 at 5:05 pm

Thanks, Paul. Yes. I fixed the half.

While it’s true that F-tests are one-tailed, they’re not testing directional hypotheses, the way a one-tailed t or z test does.

October 29, 2014 at 11:15 pm

I trying to complete the results of my study and need to know how to convert my 1-tailed results to 2-tailed? The company that ran my stats used a 1-tailed instead of 2-tailed, which as I understand is what I should have used to show directionality. Nicole

November 3, 2014 at 4:41 pm

Hi Nicole, just double them.

September 26, 2014 at 8:13 am

Very interesting. I am reading a much celebrated book (The weakness of Civil Society in Post-Communist Europe, by Marc Howard, 2003) in polticial science at the moment containing regression analysis with one tailed coeficients. This, togheter with that they only have a N of 23 (with 5 independent variables), raise my eyebrows. The results are also quite controversial….

What would you off-hand say about that?

September 26, 2014 at 12:27 pm

Without knowing anything else, the one tailed tests of coefficients wouldn’t worry me too much except for the fact you said it’s controversial. Which means perhaps the opposite result is reasonable.

The N of 23 is more of a concern. That’s pretty small. I find results like this are not bad per se. It’s fine to consider as one possible piece of information–they are great for spurring more research. But you can’t make any conclusions based on them.

June 14, 2013 at 9:54 pm

Jst wanna thank you for your post ; it will save me on my exam tomorrow (y)

& in my course it is not always divided by 2 (p-value). It depends on the value of your t (,= 0) & if your H1:’value” is > or 0, Hasard ?

Even if i’m wrong, I want to thank you again !

January 2, 2013 at 8:16 am

how can we calculate p-value of one-tailed from two-tailed hypothesis in spss?

January 2, 2013 at 10:50 am

All you have to do is divide it by 2.

December 1, 2012 at 11:04 am

i found this issue more important please continue in such way. kind regards, nurilign.

August 27, 2010 at 12:59 pm

Thank you so much for your reply and offer to try a find a source regarding the limited utility of one-tailed tests when doing ANOVAs and regressions, as well as the advice for converting two-tailed tests to one-tailed tests.

The only problem I have is that the Pearson Correlation Coefficient output from my stats consultant does not contain the p value. In order to calculate the p value for a two-tailed test, I thought it might be possible to take the df (n-2 for two-tailed tests) and look up the significance level in the table of critical values of the correlation coefficient. Once I get those values, I would simply divide by 2 to get the one-tailed level of significance. Do you think that is a statistically sound procedure.

Thank you again for your assistance, Sue

August 25, 2010 at 11:29 am

I am currently working on my dissertation and one of my committee members suggested that I should have used a one-tailed test as I have a directional hypothesis, but I think that a two-tailed test is just as appropriate based on several of the reasons listed on the blog.

I was particularly intrigued by the statement that “F tests, chi-square tests, etc. can’t accommodate one-tailed tests because their distributions are not symmetric.” This would make a fine argument for not re-rerunning my data and was wondering if there is a reference or citation for that point. I have not been able to find that point in any of the stats texts that I own. Any help would be greatly appreciated!

August 26, 2010 at 7:23 pm

Hi Sue, Hmmm. I would think that texts that talk about the F-test would mention that it's not symmetric. You could certainly use any text that states that t-squared=F. But I'll see if I can find something that says it directly. But in any case, you don't have to rerun anything, even if you weren't using F tests. To get a one-sided p-value, just halve the two-sided p-value you have. Karen

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Fisher, Neyman-Pearson or NHST? A tutorial for teaching data testing

Despite frequent calls for the overhaul of null hypothesis significance testing (NHST), this controversial procedure remains ubiquitous in behavioral, social and biomedical teaching and research. Little change seems possible once the procedure becomes well ingrained in the minds and current practice of researchers; thus, the optimal opportunity for such change is at the time the procedure is taught, be this at undergraduate or at postgraduate levels. This paper presents a tutorial for the teaching of data testing procedures, often referred to as hypothesis testing theories. The first procedure introduced is Fisher's approach to data testing—tests of significance; the second is Neyman-Pearson's approach—tests of acceptance; the final procedure is the incongruent combination of the previous two theories into the current approach—NSHT. For those researchers sticking with the latter, two compromise solutions on how to improve NHST conclude the tutorial.

Introduction

This paper introduces the classic approaches for testing research data: tests of significance, which Fisher helped develop and promote starting in 1925; tests of statistical hypotheses, developed by Neyman and Pearson ( 1928 ); and null hypothesis significance testing (NHST), first concocted by Lindquist ( 1940 ). This chronological arrangement is fortuitous insofar it introduces the simpler testing approach by Fisher first, then moves onto the more complex one by Neyman and Pearson, before tackling the incongruent hybrid approach represented by NHST (Gigerenzer, 2004 ; Hubbard, 2004 ). Other theories, such as Bayes's hypotheses testing (Lindley, 1965 ) and Wald's ( 1950 ) decision theory, are not object of this tutorial.

The main aim of the tutorial is to illustrate the bases of discord in the debate against NHST (Macdonald, 2002 ; Gigerenzer, 2004 ), which remains a problem not only yet unresolved but very much ubiquitous in current data testing (e.g., Franco et al., 2014 ) and teaching (e.g., Dancey and Reidy, 2014 ), especially in the biological sciences (Lovell, 2013 ; Ludbrook, 2013 ), social sciences (Frick, 1996 ), psychology (Nickerson, 2000 ; Gigerenzer, 2004 ) and education (Carver, 1978 , 1993 ).

This tutorial is appropriate for the teaching of data testing at undergraduate and postgraduate levels, and is best introduced when students are knowledgeable on important background information regarding research methods (such as random sampling) and inferential statistics (such as frequency distributions of means).

In order to improve understanding, statistical constructs that may bring about confusion between theories are labeled differently, attending to their function in preference to their historical use (Perezgonzalez, 2014 ). Descriptive notes (notes) and caution notes (caution) are provided to clarify matters whenever appropriate.

Fisher's approach to data testing

Ronald Aylmer Fisher was the main force behind tests of significance (Neyman, 1967 ) and can be considered the most influential figure in the current approach to testing research data (Hubbard, 2004 ). Although some steps in Fisher's approach may be worked out a priori (e.g., the setting of hypotheses and levels of significance), the approach is eminently inferential and all steps can be set up a posteriori, once the research data are ready to be analyzed (Fisher, 1955 ; Macdonald, 1997 ). Some of these steps can even be omitted in practice, as it is relatively easy for a reader to recreate them. Fisher's approach to data testing can be summarized in the five steps described below.

Step 1–Select an appropriate test . This step calls for selecting a test appropriate to, primarily, the research goal of interest (Fisher, 1932 ), although you may also need to consider other issues, such as the way your variables have been measured. For example, if your research goal is to assess differences in the number of people in two independent groups, you would choose a chi-square test (it requires variables measured at nominal levels); on the other hand, if your interest is to assess differences in the scores that the people in those two groups have reported on a questionnaire, you would choose a t -test (it requires variables measured at interval or ratio levels and a close-to-normal distribution of the groups' differences).

Step 2–Set up the null hypothesis (H 0 ) . The null hypothesis derives naturally from the test selected in the form of an exact statistical hypothesis (e.g., H 0 : M1–M2 = 0; Neyman and Pearson, 1933 ; Carver, 1978 ; Frick, 1996 ). Some parameters of this hypothesis, such as variance and degrees of freedom, are estimated from the sample, while other parameters, such as the distribution of frequencies under a particular distribution, are deduced theoretically. The statistical distribution so established thus represents the random variability that is theoretically expected for a statistical nil hypothesis (i.e., H 0 = 0) given a particular research sample (Fisher, 1954 , 1955 ; Bakan, 1966 ; Macdonald, 2002 ; Hubbard, 2004 ). It is called the null hypothesis because it stands to be nullified with research data (Gigerenzer, 2004 ).

Among things to consider when setting the null hypothesis is its directionality.

Directional and non-directional hypotheses . With some research projects, the direction of the results is expected (e.g., one group will perform better than the other). In these cases, a directional null hypothesis covering all remaining possible results can be set (e.g., H 0 : M1–M2 = 0). With other projects, however, the direction of the results is not predictable or of no research interest. In these cases, a non-directional hypothesis is most suitable (e.g., H 0 : M1–M2 = 0).

H 0 does not need to be a nil hypothesis, that is, one that always equals zero (Fisher, 1955 ; Gigerenzer, 2004 ). For example, H 0 could be that the group difference is not larger than certain value (Newman et al., 2001 ). More often than not, however, H 0 tends to be zero.

Setting up H 0 is one of the steps usually omitted if following the typical nil expectation (e.g., no correlation between variables, no differences in variance among groups, etc.). Even directional nil hypotheses are often omitted, instead specifying that one-tailed tests (see below) have been used in the analysis.

Step 3-Calculate the theoretical probability of the results under H 0 ( p ) . Once the corresponding theoretical distribution is established, the probability ( p -value) of any datum under the null hypothesis is also established, which is what statistics calculate (Fisher, 1955 , 1960 ; Bakan, 1966 ; Johnstone, 1987 ; Cortina and Dunlap, 1997 ; Hagen, 1997 ). Data closer to the mean of the distribution (Figure ​ (Figure1) 1 ) have a greater probability of occurrence under the null distribution; that is, they appear more frequently and show a larger p -value (e.g., p = 0.46, or 46 times in a 100 trials). On the other hand, data located further away from the mean have a lower probability of occurrence under the null distribution; that is, they appear less often and, thus, show a smaller p -value (e.g., p = 0.003). Of interest to us is the probability of our research results under such null distribution (e.g., the probability of the difference in means between two research groups).

Location of a t -value and its corresponding p -value on a theoretical t distribution with 30 degrees of freedom . The actual p -value conveys stronger evidence against H 0 than sig ≈0.05 and can be considered highly significant.

The p -value comprises the probability of the observed results and also of any other more extreme results (e.g., the probability of the actual difference between groups and any other difference more extreme than that). Thus, the p -value is a cumulative probability rather than an exact point probability: It covers the probability area extending from the observed results toward the tail of the distribution (Fisher, 1960 ; Carver, 1978 ; Frick, 1996 ; Hubbard, 2004 ).

P-values provide information about the theoretical probability of the observed and more extreme results under a null hypothesis assumed to be true (Fisher, 1960 ; Bakan, 1966 ), or, said otherwise, the probability of the data given a true hypothesis—P(D|H); (Carver, 1978 ; Hubbard, 2004 ). As H 0 is always true (i.e., it shows the theoretical random distribution of frequencies under certain parameters), it cannot, at the same time, be false nor falsifiable a posteriori. Basically, if at any point you say that H 0 is false, then you are also invalidating the whole test and its results. Furthermore, because H 0 is always true, it cannot be proved, either.

Step 4–Assess the statistical significance of the results . Fisher proposed tests of significance as a tool for identifying research results of interest, defined as those with a low probability of occurring as mere random variation of a null hypothesis. A research result with a low p -value may, thus, be taken as evidence against the null (i.e., as evidence that it may not explain those results satisfactorily; Fisher, 1960 ; Bakan, 1966 ; Johnstone, 1987 ; Macdonald, 2002 ). How small a result ought to be in order to be considered statistically significant is largely dependent on the researcher in question, and may vary from research to research (Fisher, 1960 ; Gigerenzer, 2004 ). The decision can also be left to the reader, so reporting exact p -values is very informative (Fisher, 1973 ; Macdonald, 1997 ; Gigerenzer, 2004 ).

Overall, however, the assessment of research results is largely made bound to a given level of significance, by comparing whether the research p -value is smaller than such level of significance or not (Fisher, 1954 , 1960 ; Johnstone, 1987 ):

• If the p -value is approximately equal to or smaller than the level of significance, the result is considered statistically significant.
• If the p -value is larger than the level of significance, the result is considered statistically non-significant.

Among things to consider when assessing the statistical significance of research results are the level of significance, and how it is affected by the directionality of the test and other corrections.

Level of significance (sig) . The level of significance is a theoretical p -value used as a point of reference to help identify statistically significant results (Figure ​ (Figure1). 1 ). There is no need to set up a level of significance a priori nor for a particular level of significance to be used in all occasions, although levels of significance such as 5% (sig ≈0.05) or 1% (sig ≈0.01) may be used for convenience, especially with novel research projects (Fisher, 1960 ; Carver, 1978 ; Gigerenzer, 2004 ). This highlights an important property of Fisher's levels of significance: They do not need to be rigid (e.g., p -values such as 0.049 and 0.051 have about the same statistical significance around a convenient level of significance of 5%; Johnstone, 1987 ).

Another property of tests of significance is that the observed p -value is taken as evidence against the null hypothesis, so that the smaller the p -value the stronger the evidence it provides (Fisher, 1960 ; Spielman, 1978 ). This means that it is plausible to gradate the strength of such evidence with smaller levels of significance. For example, if using 5% (sig ≈0.05) as a convenient level for identifying results which are just significant, then 1% (sig ≈0.01) may be used as a convenient level for identifying highly significant results and 1‰ (sig ≈0.001) for identifying extremely significant results.

Setting up a level of significance is another step usually omitted. In such cases, you may assume the researcher is using conventional levels of significance.

If both H 0 and sig are made explicit, they could be joined in a single postulate, such as H 0 : M 1 –M 2 = 0, sig ≈0.05.

Notice that the p-value informs about the probability associated with a given test value (e.g., a t value). You could use this test value to decide about the significance of your results in a fashion similar to Neyman-Pearson's approach (see below). However, you get more information about the strength of the research evidence with p-values.

Although the p-value is the most informative statistic of a test of significance, in psychology (e.g., American Psychological Association, 2010 ) you also report the research value of the test—e.g., t (30) = 2.25, p = 0.016, 1-tailed. Albeit cumbersome and largely ignored by the reader, the research value of the test offers potentially useful information (e.g., about the valid sample size used with a test).

Caution: Be careful not to interpret Fisher's p-values as Neyman-Pearson's Type I errors (α, see below). Probability values in single research projects are not the same than probability values in the long run (Johnstone, 1987 ), something illustrated by Berger ( 2003 )—who reported that p = 0.05 often corresponds to α = 0.5 (or anywhere between α = 0.22 and α > 0.5)—and Cumming ( 2014 )—who simulates the “dance” of p-values in the long run, commented further in Perezgonzalez ( 2015 ).

One-tailed and two-tailed tests . With some tests (e.g., F -tests) research data can only be tested against one side of the null distribution (one-tailed tests), while other tests (e.g., t -tests) can test research data against both sides of the null distribution at the same time. With one-tailed tests you set the level of significance on the appropriate tail of the distribution. With two-tailed tests you cover both eventualities by dividing the level of significance between both tails (Fisher, 1960 ; Macdonald, 1997 ), which is commonly done by halving the total level of significance in two equal areas (thus covering, for example, the 2.5% most extreme positive differences and the 2.5% most extreme negative differences).

The tail of a test depends on the test in question, not on whether the null hypothesis is directional or non-directional. However, you can use two-tailed tests as one-tailed ones when testing data against directional hypotheses.

Correction of the level of significance for multiple tests . As we introduced earlier, a p -value can be interpreted in terms of its expected frequency of occurrence under the specific null distribution for a particular test (e.g., p = 0.02 describes a result that is expected to appear 2 times out of 100 under H 0 ). The same goes for theoretical p -values used as levels of significance. Thus, if more than one test is performed, this has the consequence of also increasing the probability of finding statistical significant results which are due to mere chance variation. In order to keep such probability at acceptable levels overall, the level of significance may be corrected downwards (Hagen, 1997 ). A popular correction is Bonferroni's, which reduces the level of significance proportionally to the number of tests carried out. For example, if your selected level of significance is 5% (sig ≈0.05) and you carry out two tests, then such level of significance is maintained overall by correcting the level of significance for each test down to 2.5% (sig ≈0.05/2 tests ≈0.025, or 2.5% per test).

Bonferroni's correction is popular but controversial, mainly because it is too conservative, more so as the number of multiple tests increases. There are other methods for controlling the probability of false results when doing multiple comparisons, including familywise error rate methods (e.g., Holland and Copenhaver, 1987 ), false discovery rate methods (e.g., Benjamini and Hochberg, 1995 ), resampling methods (jackknifing, bootstrapping— e.g., Efron, 1981 ), and permutation tests (i.e., exact tests—e.g., Gill, 2007 ).

Step 5–Interpret the statistical significance of the results . A significant result is literally interpreted as a dual statement: Either a rare result that occurs only with probability p (or lower) just happened, or the null hypothesis does not explain the research results satisfactorily (Fisher, 1955 ; Carver, 1978 ; Johnstone, 1987 ; Macdonald, 1997 ). Such literal interpretation is rarely encountered, however, and most common interpretations are in the line of “The null hypothesis did not seem to explain the research results well, thus we inferred that other processes—which we believe to be our experimental manipulation—exist that account for the results,” or “The research results were statistically significant, thus we inferred that the treatment used accounted for such difference.”

Non-significant results may be ignored (Fisher, 1960 ; Nunnally, 1960 ), although they can still provide useful information, such as whether results were in the expected direction and about their magnitude (Fisher, 1955 ). In fact, although always denying that the null hypothesis could ever be supported or established, Fisher conceded that non-significant results might be used for confirming or strengthening it (Fisher, 1955 ; Johnstone, 1987 ).

Statistically speaking, Fisher's approach only ascertains the probability of the research data under a null hypothesis. Doubting or denying such hypothesis given a low p-value does not necessarily “support” or “prove” that the opposite is true (e.g., that there is a difference or a correlation in the population). More importantly, it does not “support” or “prove” that whatever else has been done in the research (e.g., the treatment used) explains the results, either (Macdonald, 1997 ). For Fisher, a good control of the research design (Fisher, 1955 ; Johnstone, 1987 ; Cortina and Dunlap, 1997 ), especially random allocation, is paramount to make sensible inferences based on the results of tests of significance (Fisher, 1954 ; Neyman, 1967 ). He was also adamant that, given a significant result, further research was needed to establish that there has indeed been an effect due to the treatment used (Fisher, 1954 ; Johnstone, 1987 ; Macdonald, 2002 ). Finally, he considered significant results as mere data points and encouraged the use of meta-analysis for progressing further, combining significant and non-significant results from related research projects (Fisher, 1960 ; Neyman, 1967 ).

Highlights of Fisher's approach

Flexibility . Because most of the work is done a posteriori, Fisher's approach is quite flexible, allowing for any number of tests to be carried out and, therefore, any number of null hypotheses to be tested (a correction of the level of significance may be appropriate, though—Macdonald, 1997 ).

Better suited for ad-hoc research projects . Given above flexibility, Fisher's approach is well suited for single, ad-hoc , research projects (Neyman, 1956 ; Johnstone, 1987 ), as well as for exploratory research (Frick, 1996 ; Macdonald, 1997 ; Gigerenzer, 2004 ).

Inferential . Fisher's procedure is largely inferential, from the sample to the population of reference, albeit of limited reach, mainly restricted to populations that share parameters similar to those estimated from the sample (Fisher, 1954 , 1955 ; Macdonald, 2002 ; Hubbard, 2004 ).

No power analysis . Neyman ( 1967 ) and Kruskal and Savage (Kruskal, 1980 ) were surprised that Fisher did not explicitly attend to the power of a test. Fisher talked about sensitiveness, a similar concept, and how it could be increased by increasing sample size (Fisher, 1960 ). However, he never created a mathematical procedure for controlling sensitiveness in a predictable manner (Macdonald, 1997 ; Hubbard, 2004 ).

No alternative hypothesis . One of the main critiques to Fisher's approach is the lack of an explicit alternative hypothesis (Macdonald, 2002 ; Gigerenzer, 2004 ; Hubbard, 2004 ), because there is no point in rejecting a null hypothesis without an alternative explanation being available (Pearson, 1990 ). However, Fisher considered alternative hypotheses implicitly—these being the negation of the null hypotheses—so much so that for him the main task of the researcher—and a definition of a research project well done—was to systematically reject with enough evidence the corresponding null hypothesis (Fisher, 1960 ).

Neyman-Pearson's approach to data testing

Jerzy Neyman and Egon Sharpe Pearson tried to improve Fisher's procedure (Fisher, 1955 ; Pearson, 1955 ; Jones and Tukey, 2000 ; Macdonald, 2002 ) and ended up developing an alternative approach to data testing. Neyman-Pearson's approach is more mathematical than Fisher's and does much of its work a priori, at the planning stage of the research project (Fisher, 1955 ; Macdonald, 1997 ; Gigerenzer, 2004 ; Hubbard, 2004 ). It also introduces a number of constructs, some of which are similar to those of Fisher. Overall, Neyman-Pearson's approach to data testing can be considered tests of acceptance (Fisher, 1955 ; Pearson, 1955 ; Spielman, 1978 ; Perezgonzalez, 2014 ), summarized in the following eight main steps.

A priori steps

Step 1–Set up the expected effect size in the population . The main conceptual innovation of Neyman-Pearson's approach was the consideration of explicit alternative hypotheses when testing research data (Neyman and Pearson, 1928 , 1933 ; Neyman, 1956 ; Macdonald, 2002 ; Gigerenzer, 2004 ; Hubbard, 2004 ). In their simplest postulate, the alternative hypothesis represents a second population that sits alongside the population of the main hypothesis on the same continuum of values. These two groups differ by some degree: the effect size (Cohen, 1988 ; Macdonald, 1997 ).

Although the effect size was a new concept introduced by Neyman and Pearson, in psychology it was popularized by Cohen ( 1988 ). For example, Cohen's conventions for capturing differences between groups—d (Figure ​ (Figure2)—were 2 )—were based on the degree of visibility of such differences in the population: the smaller the effect size, the more difficult to appreciate such differences; the larger the effect size, the easier to appreciate such differences. Thus, effect sizes also double as a measure of importance in the real world (Nunnally, 1960 ; Cohen, 1988 ; Frick, 1996 ).

A conventional large difference—Cohen's d = 0.8 —between two normally distributed populations, as a fraction of one standard deviation .

When testing data about samples, however, statistics do not work with unknown population distributions but with distributions of samples, which have narrower standard errors. In these cases, the effect size can still be defined as above because the means of the populations remain unaffected, but the sampling distributions would appear separated rather than overlapping (Figure ​ (Figure3). 3 ). Because we rarely know the parameters of populations, it is their equivalent effect size measures in the context of sampling distributions which are of interest.

Sampling distributions ( N = 50 each) of the populations in Figure ​ Figure2 2 . MES ( d = 0.8), assuming β = 0.20, is d = 0.32 (i.e., the expected difference in the population ranges between d = 0.32 and infinity).

As we shall see below, the alternative hypothesis is the one that provides information about the effect size to be expected. However, because this hypothesis is not tested, Neyman-Pearson's procedure largely ignores its distribution except for a small percentage of it, which is called “beta” (β; Gigerenzer, 2004 ). Therefore, it is easier to understand Neyman-Pearson's procedure if we peg the effect size to beta and call it the expected minimum effect size (MES; Figure ​ Figure3). 3 ). This helps us conceptualize better how Neyman-Pearson's procedure works (Schmidt, 1996 ): The minimum effect size effectively represents that part of the main hypothesis that is not going to be rejected by the test ( i.e., MES captures values of no research interest which you want to leave under H M ; Cortina and Dunlap, 1997 ; Hagen, 1997 ; Macdonald, 2002 ). (Worry not, as there is no need to perform any further calculations: The population effect size is the one to use, for example, for estimating research power.)

A particularity of Neyman-Pearson's approach is that the two hypotheses are assumed to represent defined populations, the research sample being an instance of either of them (i.e., they are populations of samples generated by repetition of a common random process—Neyman and Pearson, 1928 ; Pearson, 1955 ; Hagen, 1997 ; Hubbard, 2004 ). This is unlike Fisher's population, which can be considered more theoretical, generated ad-hoc so as for providing the appropriate random distribution for the research sample at hand (i.e., a population of samples similar to the research sample—Fisher, 1955 ; Johnstone, 1987 ).

Step 2–Select an optimal test . As we shall see below, another of Neyman-Pearson's contributions was the construct of the power of a test. A spin-off of this contribution is that it has been possible to establish which tests are most powerful (for example, parametric tests are more powerful than non-parametric tests, and one-tailed tests are more powerful than two-tailed tests), and under which conditions (for example, increasing sample size increases power). For Neyman and Pearson, thus, you are better off choosing the most powerful test for your research project (Neyman, 1942 , 1956 ).

Step 3–Set up the main hypothesis (H M ) . Neyman-Pearson's approach considers, at least, two competing hypotheses, although it only tests data under one of them. The hypothesis which is the most important for the research (i.e., the one you do not want to reject too often) is the one tested (Neyman and Pearson, 1928 ; Neyman, 1942 ; Spielman, 1973 ). This hypothesis is better off written so as for incorporating the minimum expected effect size within its postulate (e.g., H M : M 1 –M 2 = 0 ± MES), so that it is clear that values within such minimum threshold are considered reasonably probable under the main hypothesis, while values outside such minimum threshold are considered as more probable under the alternative hypothesis (Figure ​ (Figure4 4 ).

Neyman-Pearson's approach tests data under H M using the rejection region delimited by α . H A contributes MES and β. Differences of research interest will be equal or larger than MES and will fall within this rejection region.

Neyman-Pearson's H M is very similar to Fisher's H 0 . Indeed, Neyman and Pearson also called it the null hypothesis and often postulated it in a similar manner (e.g., as H M : M 1 –M 2 = 0). However, this similarity is merely superficial on three accounts: H M needs to be considered at the design stage (H 0 is rarely made explicit); it is implicitly designed to incorporate any value below the MES—i.e., the a priori power analysis of a test aims to capture such minimum difference (effect sizes are not part of Fisher's approach); and it is but one of two competing explanations for the research results (H 0 is the only hypothesis, to be nullified with evidence).

The main aspect to consider when setting the main hypothesis is the Type I error you want to control for during the research.

Type I error . A Type I error (or error of the first class) is made every time the main hypothesis is wrongly rejected (thus, every time the alternative hypothesis is wrongly accepted). Because the hypothesis under test is your main hypothesis, this is an error that you want to minimize as much as possible in your lifetime research (Neyman and Pearson, 1928 , 1933 ; Neyman, 1942 ; Macdonald, 1997 ).

A Type I error is possible under Fisher's approach, as it is similar to the error made when rejecting H 0 (Carver, 1978 ). However, this similarity is merely superficial on two accounts: Neyman and Pearson considered it an error whose relevance only manifests itself in the long run because it is not possible to know whether such an error has been made in any particular trial (Fisher's approach is eminently ad-hoc, so the risk of a long-run Type I error is of little relevance); therefore, it is an error that needs to be considered and minimized at the design stage of the research project in order to ensure good power—you cannot minimize this error a posteriori (with Fisher's approach, the potential impact of errors on individual projects is better controlled by correcting the level of significance as appropriate, for example, with a Bonferroni correction).

Alpha (α) . Alpha is the probability of committing a Type I error in the long run (Gigerenzer, 2004 ). Neyman and Pearson often worked with convenient alpha levels such as 5% (α = 0.05) and 1% (α = 0.01), although different levels can also be set. The main hypothesis can, thus, be written so as for incorporating the alpha level in its postulate (e.g., H M : M 1 –M 2 = 0 ± MES, α = 0.05), to be read as the probability level at which the main hypothesis will be rejected in favor of the alternative hypothesis.

Neyman-Pearson's α looks very similar to Fisher's sig. Indeed, Neyman and Pearson also called it the significance level of the test and used the same conventional cut-off points (5, 1%). However, this similarity is merely superficial on three accounts: α needs to be set a priori (not necessarily so under Fisher's approach); Neyman-Pearson's approach is not a test of significance (they are not interested in the strength of the evidence against H M ) but a test of acceptance (deciding whether to accept H A instead of H M ); and α does not admit gradation—i.e., you may choose, for example, either α = 0.05 or α = 0.01, but not both, for the same test (while with Fisher's approach you can have different levels of more extreme significance).

The critical region (CR test ) and critical value (CV test , Test crit ) of a test . The alpha level helps draw a critical region, or rejection region (Figure ​ (Figure4), 4 ), on the probability distribution of the main hypothesis (Neyman and Pearson, 1928 ). Any research value that falls outside this critical region will be taken as reasonably probable under the main hypothesis, and any research result that falls within the critical region will be taken as most probable under the alternative hypothesis. The alpha level, thus, also helps identify the location of the critical value of such test, the boundary for deciding between hypotheses. Thus, once the critical value is known—see below—, the main hypothesis can also be written so as for incorporating such critical value, if so desired (e.g., H M : M 1 –M 2 = 0 ± MES, α = 0.05, CV t = 2.38).

Neyman-Pearson's critical region is very similar to the equivalent critical region you would obtain by using Fisher's sig as a cut-off point on a null distribution. However, this similarity is rather unimportant on three accounts: it is based on a critical value which delimits the region to reject H M in favor of H A , irrespective of the actual observed value of the test (Fisher, on the contrary, is more interested in the actual p-value of the research result); it is fixed a priori and, thus, rigid and immobile (Fisher's level of significance can be flexible—Macdonald, 2002 ); and it is non-gradable (with Fisher's approach, you may delimit several more extreme critical regions as areas of stronger evidence).

Step 4–Set up the alternative hypothesis (H A ) . One of the main innovations of Neyman-Pearson's approach was the consideration of alternative hypotheses (Neyman and Pearson, 1928 , 1933 ; Neyman, 1956 ). Unfortunately, the alternative hypothesis is often postulated in an unspecified manner (e.g., as H A : M 1 –M 2 ≠ 0), even by Neyman and Pearson themselves (Macdonald, 1997 ; Jones and Tukey, 2000 ). In practice, a fully specified alternative hypothesis (e.g., its mean and variance) is not necessary because this hypothesis only provides partial information to the testing of the main hypothesis (a.k.a., the effect size and β). Therefore, the alternative hypothesis is better written so as for incorporating the minimum effect size within its postulate (e.g., H A : M 1 –M 2 ≠ 0 ± MES). This way it is clear that values beyond such minimum effect size are the ones considered of research importance.

Neyman-Pearson's H A is often postulated as the negation of a nil hypothesis (H A : M 1 –M 2 ≠ 0), which is coherent with a simple postulate of H M (H M : M 1 –M 2 = 0). These simplified postulates are not accurate and are easily confused with Fisher's approach to data testing—H M resembles Fisher's H 0 , and H A resembles a mere negation of H 0 . However, merely negating H 0 does not make its negation a valid alternative hypothesis—otherwise Fisher would have put forward such alternative hypothesis, something which he was vehemently against (Hubbard, 2004 ). As discussed earlier, Neyman-Pearson's approach introduces the construct of effect size into their testing approach; thus, incorporating such construct in the specification of both H M and H A makes them more accurate, and less confusing, than their simplified versions.

Among things to consider when setting the alternative hypothesis are the expected effect size in the population (see above) and the Type II error you are prepared to commit.

Type II error . A Type II error (or error of the second class) is made every time the main hypothesis is wrongly retained (thus, every time H A is wrongly rejected). Making a Type II error is less critical than making a Type I error, yet you still want to minimize the probability of making this error once you have decided which alpha level to use (Neyman and Pearson, 1933 ; Neyman, 1942 ; Macdonald, 2002 ).

Beta (β) . Beta is the probability of committing a Type II error in the long run and is, therefore, a parameter of the alternative hypothesis (Figure ​ (Figure4, 4 , Neyman, 1956 ). You want to make beta as small as possible, although not smaller than alpha (if β needed to be smaller than α, then H A should be your main hypothesis, instead!). Neyman and Pearson proposed 20% (β = 0.20) as an upper ceiling for beta, and the value of alpha (β = α) as its lower floor (Neyman, 1953 ). For symmetry with the main hypothesis, the alternative hypothesis can, thus, be written so as for incorporating the beta level in its postulate (e.g., H A : M 1 –M 2 ≠ 0 ± MES, β = 0.20).

Step 5–Calculate the sample size (N) required for good power (1–β) . Neyman-Pearson's approach is eminently a priori in order to ensure that the research to be done has good power (Neyman, 1942 , 1956 ; Pearson, 1955 ; Macdonald, 2002 ). Power is the probability of correctly rejecting the main hypothesis in favor of the alternative hypothesis (i.e., of correctly accepting H A ). It is the mathematical opposite of the Type II error (thus, 1–β; Macdonald, 1997 ; Hubbard, 2004 ). Power depends on the type of test selected (e.g., parametric tests and one-tailed tests increase power), as well as on the expected effect size (larger ES's increase power), alpha (larger α's increase power) and beta (smaller β 's increase power). A priori power is ensured by calculating the correct sample size given those parameters (Spielman, 1973 ). Because power is the opposite of beta, the lower floor for good power is, thus, 80% (1–β = 0.80), and its upper ceiling is 1–alpha (1–β = 1–α).

H A does not need to be tested under Neyman-Pearson's approach, only H M (Neyman and Pearson, 1928 , 1933 ; Neyman, 1942 ; Pearson, 1955 ; Spielman, 1973 ). Therefore, the procedure looks similar to Fisher's and, under similar circumstances (e.g., when using the same test and sample size), it will lead to the same results. The main difference between procedures is that Neyman-Pearson's H A provides explicit information to the test; that is, information about ES and β. If this information is not taken into account for designing a research project with adequate power, then, by default, you are carrying out a test under Fisher's approach.

Caution: For Neyman and Pearson, there is little justification in carrying out research projects with low power. When a research project has low power, Type II errors are too big, so it is less probable to reject H M in favor of H A , while, at the same time, it makes unreasonable to accept H M as the best explanation for the research results. If you face a research project with low a priori power, try the best compromise between its parameters (such as increasing α, relaxing β, settling for a larger ES, or using one-tailed tests; Neyman and Pearson, 1933 ). If all fails, consider Fisher's approach, instead.

Step 6–Calculate the critical value of the test (CV test , or Test crit ) . Some of above parameters (test, α and N) can be used for calculating the critical value of the test; that is, the value to be used as the cut-off point for deciding between hypotheses (Figure ​ (Figure5, 5 , Neyman and Pearson, 1933 ).

Neyman-Pearson's test in action: CV test is the point for deciding between hypotheses; it coincides with the cut-off points underlying α, β, and MES .

A posteriori steps

Step 7–Calculate the test value for the research (RV test ) . In order to carry out the test, some unknown parameters of the populations are estimated from the sample (e.g., variance), while other parameters are deduced theoretically (e.g., the distribution of frequencies under a particular statistical distribution). The statistical distribution so established thus represents the random variability that is theoretically expected for a statistical main hypothesis given a particular research sample, and provides information about the values expected at different locations under such distribution.

By applying the corresponding formula, the research value of the test (RV test ) is obtained. This value is closer to zero the closer the research data is to the mean of the main hypothesis; it gets larger the further away the research data is from the mean of the main hypothesis.

P-values can also be used for testing data when using Neyman-Pearson's approach, as testing data under H M is similar to testing data under Fisher's H 0 (Fisher, 1955 ). It implies calculating the theoretical probability of the research data under the distribution of H M — P ( D | H M ). Just be mindful that p-values go in the opposite way than RVs, with larger p-values being closer to H M and smaller p-values being further away from it.

Caution: Because of above equivalence, you may use p-values instead of CV test with Neyman-Pearson's approach. However, p-values need to be considered mere proxies under this approach and, thus, have no evidential properties whatsoever (Frick, 1996 ; Gigerenzer, 2004 ). For example, if working with a priori α = 0.05, p = 0.01 would lead you to reject H M at α = 0.05; however, it would be incorrect to reject it at α = 0.01 (i.e., α cannot be adjusted a posteriori), and it would be incorrect to conclude that you reject H M strongly (i.e., α cannot be gradated). If confused, you are better off sticking to CV test , and using p-values only with Fisher's approach.

Step 8–Decide in favor of either the main or the alternative hypothesis . Neyman-Pearson's approach is rather mechanical once the a priori steps have been satisfied (Neyman and Pearson, 1933 ; Neyman, 1942 , 1956 ; Spielman, 1978 ; Macdonald, 2002 ). Thus, the analysis is carried out as per the optimal test selected and the interpretation of results is informed by the mathematics of the test, following on the a priori pattern set up for deciding between hypotheses:

• If the observed result falls within the critical region, reject the main hypothesis and accept the alternative hypothesis.
• If the observed result falls outside the critical region and the test has good power, accept the main hypothesis.
• If the observed result falls outside the critical region and the test has low power, conclude nothing. (Ideally, you would not carry out research with low power—Neyman, 1955 ).

Neyman-Pearson's approach leads to a decision between hypotheses (Neyman and Pearson, 1933 ; Spielman, 1978 ). In principle, this decision should be between rejecting H M or retaining H M (assuming good power), as the test is carried out on H M only (Neyman, 1942 ). In practice, it does not really make much difference whether you accept H M or H A , as appropriate (Macdonald, 1997 ). In fact, accepting either H M or H A is beneficial as it prevents confusion with Fisher's approach, which can only reject H 0 (Perezgonzalez, 2014 ).

Reporting the observed research test value is relevant under Neyman-Pearson's approach, as it serves to compare the observed value against the a priori critical value—e.g., t (64) = 3.31, 1-tailed > CV t = 2.38, thus accept H A . When using a p-value as a proxy for CV test , simply strip any evidential value off p—e.g., t (64) = 3.31, p < α, 1-tailed.

Neyman-Pearson's hypotheses are also assumed to be true. H M represents the probability distribution of the data given a true hypothesis—P(D|H M ), while H A represents the distribution of the data under an alternative true hypothesis—P(D|H A ), even when it is never tested. This means that H M and H A cannot be, at the same time false, nor proved or falsified a posteriori. The only way forward is to act as if the conclusion reached by the test was true—subject to a probability α or β of making a Type I or Type II error, respectively (Neyman and Pearson, 1933 ; Cortina and Dunlap, 1997 ).

Highlights of Neyman-Pearson's approach

More powerful . Neyman-Pearson's approach is more powerful than Fisher's for testing data in the long run (Williams et al., 2006 ). However, repeated sampling is rare in research (Fisher, 1955 ).

Better suited for repeated sampling projects . Because of above, Neyman-Pearson's approach is well-suited for repeated sampling research using the same population and tests, such as industrial quality control or large scale diagnostic testing (Fisher, 1955 ; Spielman, 1973 ).

Deductive . The approach is deductive and rather mechanical once the a priori steps have been set up (Neyman and Pearson, 1933 ; Neyman, 1942 ; Fisher, 1955 ).

Less flexible than Fisher's approach . Because most of the work is done a priori, this approach is less flexible for accommodating tests not thought of beforehand and for doing exploratory research (Macdonald, 2002 ).

Defaults easily to Fisher's approach . As this approach looks superficially similar to Fisher's, it is easy to confuse both and forget what makes Neyman-Pearson's approach unique (Lehman, 1993 ). If the information provided by the alternative hypothesis—ES and β —is not taken into account for designing research with good power, data analysis defaults to Fisher's test of significance.

Null hypothesis significance testing

NHST is the most common procedure used for testing data nowadays, albeit under the false assumption of testing substantive hypotheses (Carver, 1978 ; Nickerson, 2000 ; Hubbard, 2004 ; Hager, 2013 ). NHST is, in reality, an amalgamation of Fisher's and Neyman-Pearson's theories, offered as a seamless approach to testing (Macdonald, 2002 ; Gigerenzer, 2004 ). It is not a clearly defined amalgamation either and, depending on the author describing it or on the researcher using it, it may veer more toward Fisher's approach (e.g., American Psychological Association, 2010 ; Nunnally, 1960 ; Wilkinson and the Task Force on Statistical Inference, 1999 ; Krueger, 2001 ) or toward Neyman-Pearson's approach (e.g., Cohen, 1988 ; Rosnow and Rosenthal, 1989 ; Frick, 1996 ; Schmidt, 1996 ; Cortina and Dunlap, 1997 ; Wainer, 1999 ; Nickerson, 2000 ; Kline, 2004 ).

Unfortunately, if we compare Fisher's and Neyman-Pearson's approaches vis-à-vis, we find that they are incompatible in most accounts (Table ​ (Table1). 1 ). Overall, however, most amalgamations follow Neyman-Pearson procedurally but Fisher philosophically (Spielman, 1978 ; Johnstone, 1986 ; Cortina and Dunlap, 1997 ; Hubbard, 2004 ).

Equivalence of constructs in Fisher's and Neyman-Pearson's theories, and amalgamation of constructs under NHST .

NHST is not only ubiquitous but very well ingrained in the minds and current practice of most researchers, journal editors and publishers (Spielman, 1978 ; Gigerenzer, 2004 ; Hubbard, 2004 ), especially in the biological sciences (Lovell, 2013 ; Ludbrook, 2013 ), social sciences (Frick, 1996 ), psychology (Nickerson, 2000 ; Gigerenzer, 2004 ) and education (Carver, 1978 , 1993 ). Indeed, most statistics textbooks for those disciplines still teach NHST rather than the two approaches of Fisher and of Neyman and Pearson as separate and rather incompatible theories (e.g., Dancey and Reidy, 2014 ). NHST has also the (false) allure of being presented as a procedure for testing substantive hypotheses (Macdonald, 2002 ; Gigerenzer, 2004 ).

In the situations in which they are most often used by researchers, and assuming the corresponding parameters are also the same, both Fisher's and Neyman-Pearson's theories work with the same statistical tools and produce the same statistical results; therefore, by extension, NHST also works with the same statistical tools and produces the same results—in practice, however, both approaches start from different starting points and lead to different outcomes (Fisher, 1955 ; Spielman, 1978 ; Berger, 2003 ). In a nutshell, the differences between Fisher's and Neyman-Pearson's theories are mostly about research philosophy and about how to interpret results (Fisher, 1955 ).

The most coherent plan of action is, of course, to follow the theory which is most appropriate for purpose, be this Fisher's or Neyman-Pearson's. It is also possible to use both for achieving different goals within the same research project (e.g., Neyman-Pearson's for tests thought of a priori, and Fisher's for exploring the data further, a posteriori), pending that those goals are not mixed up.

However, the apparent parsimony of NHST and its power to withstand threats to its predominance are also understandable. Thus, I propose two practical solutions to improve NHST: the first a compromise to improve Fisher-leaning NHST, the second a compromise to improve Neyman-Pearson-leaning NHST. A computer program such as G * Power can be used for implementing the recommendations made for both.

Improving fisher-leaning NHST

Fisher's is the closest approach to NHST; it is also the philosophy underlying common statistics packages, such as SPSS. Furthermore, because using Neyman-Pearson's concepts within NHST may be irrelevant or inelegant but hardly damaging, it requires little re-engineering. A clear improvement to NHST comes from incorporating Neyman-Pearson's constructs of effect size and of a priori sample estimation for adequate power. Estimating effect sizes (both a priori and a posteriori) ensures that researchers consider importance over mere statistical significance. A priori estimation of sample size for good power also ensures that the research has enough sensitiveness for capturing the expected effect size (Huberty, 1987 ; Macdonald, 2002 ).

Improving Neyman-Pearson-leaning NHST

NHST is particularly damaging for Neyman-Pearson's approach, simply because the latter defaults to Fisher's if important constructs are not used correctly. An importantly damaging issue is the assimilation of p -values as evidence of Type I errors and the subsequent correction of alphas to match such p -values (roving α's, Goodman, 1993 ; Hubbard, 2004 ). The best compromise for improving NHST under these circumstances is to compensate a posteriori roving alphas with a posteriori roving betas (or, if so preferred, with a posteriori roving power). Basically, if you are adjusting alpha a posteriori (roving α) to reflect both the strength of evidence (sig) and the long-run Type I error (α), you should also adjust the long-run probability of making a Type II error (roving β). Report both roving alphas and roving betas for each test, and take them into account when interpreting your research results.

NHST is very controversial, even if the controversy is not well known. A sample of helpful readings on this controversy are Christensen ( 2005 ); Hubbard ( 2004 ); Gigerenzer ( 2004 ); Goodman ( 1999 ), Louçã ( 2008 , http://www.iseg.utl.pt/departamentos/economia/wp/wp022008deuece.pdf ), Halpin and Stam ( 2006 ); Huberty ( 1993 ); Johnstone ( 1986 ), and Orlitzky ( 2012 ).

Data testing procedures represented a historical advancement for the so-called “softer” sciences, starting in biology but quickly spreading to psychology, the social sciences and education. These disciplines benefited from the principles of experimental design, the rejection of subjective probabilities and the application of statistics to small samples that Sir Ronald Fisher started popularizing in 1922 (Lehmann, 2011 ), under the umbrella of his tests of significance (e.g., Fisher, 1954 ). Two mathematical contemporaries, Jerzy Neyman and Egon Sharpe Pearson, attempted to improve Fisher's procedure and ended up developing a new theory, one for deciding between competing hypotheses (Neyman and Pearson, 1928 ), more suitable to quality control and large scale diagnostic testing (Spielman, 1973 ). Both theories had enough similarities to be easily confused (Perezgonzalez, 2014 ), especially by those less epistemologically inclined; a confusion fiercely opposed by the original authors (e.g., Fisher, 1955 )—and ever since (e.g., Nickerson, 2000 ; Lehmann, 2011 ; Hager, 2013 )—but something that irreversibly happened under the label of null hypothesis significance testing. NHST is an incompatible amalgamation of the theories of Fisher and of Neyman and Pearson (Gigerenzer, 2004 ). Curiously, it is an amalgamation that is technically reassuring despite it being, philosophically, pseudoscience. More interestingly, the numerous critiques raised against it for the past 80 years have not only failed to debunk NHST from the researcher's statistical toolbox, they have also failed to be widely known, to find their way into statistics manuals, to be edited out of journal submission requirements, and to be flagged up by peer-reviewers (e.g., Gigerenzer, 2004 ). NHST effectively negates the benefits that could be gained from Fisher's and from Neyman-Pearson's theories; it also slows scientific progress (Savage, 1957 ; Carver, 1978 , 1993 ) and may be fostering pseudoscience. The best option would be to ditch NHST altogether and revert to the theories of Fisher and of Neyman-Pearson as—and when—appropriate. For everything else, there are alternative tools, among them exploratory data analysis (Tukey, 1977 ), effect sizes (Cohen, 1988 ), confidence intervals (Neyman, 1935 ), meta-analysis (Rosenthal, 1984 ), Bayesian applications (Dienes, 2014 ) and, chiefly, honest critical thinking (Fisher, 1960 ).

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Tools, Technologies and Training for Healthcare Laboratories

• Z-Stats / Basic Statistics

Z-8: Two-Sample and Directional Hypothesis Testing

This lesson describes some refinements to the hypothesis testing approach that was introduced in the previous lesson. The truth of the matter is that the previous lesson was somewhat oversimplified in order to focus on the concept and general steps in the hypothesis testing procedure. With that background, we can now get into some of the finer points of hypothesis testing.

EdD, Assistant Professor Clinical Laboratory Science Program, University of Louisville Louisville, Kentucky October 1999

Refinements in calculations, refinements in procedure, a two-tailed test for our mice, a one-tailed test for our mice.

• Critical t-values for one-tailed and two-tailed tests
• About the Author

Two sample case

The "two sample case" is a special case in which the difference between two sample means is examined. This sounds like what we did in the last lesson, but we actually looked at the difference between an observed or sample group mean and a control group mean, which was treated as if it were a population mean (rather than an observed or sample mean). There are some fine points that need to be considered about the calculations and the procedure.

Mathematically, there are some differences in the formula that should be used. Recall the form of the equation for calculating a t-value:

where Xbar is the mean of the experimental group, and µ is a population parameter or the mean of the population. In the last lesson, we substituted the control group mean XbarB for µ. However, the control group was actually a sample from the population of all such mice (in this example) and following suit, the experimental group was also just a sample from its population. Phrasing the situation this way, there are now two sets of differences that must be considered: the difference between sample means or XbarA-XbarB and the difference between population means from which these samples came or µA-µB. Those expressions should be substituted into the t calc formula to give the proper mathematical form:

• In this new equation, the difference between means (Xbar A - XbarB) is called bias and is important in determining test accuracy, so even though this discussion is getting more complicated, the statistic that we are deriving is very important.
• When the square root is taken, the result is called the standard deviation of the difference between means , SDd , which is shown by the formula:
• The proper equation for calculating the t-value for the two-sample case then becomes:

Remember the steps for testing a hypothesis are: (1) State the hypotheses; (2) Set the criterion for rejection of Ho; (3) Compute the test statistic; (4) Decide about Ho.

• The null hypothesis can be stated as: Ho: µA = µB or µA - µB = 0. But it may be more revealing to say Ho: (XbarA-XbarB) - (µA - µB) = 0. The difference between the sample means minus the difference between the population means equals zero.
• The alternative hypothesis can be stated as: Ha: µA is not equal to µB or Ha: (XbarA-XbarB) - (µA - µB) is not equal to 0, i.e., the means of the two groups are not equal.
• The criteria for rejection or the alpha level is 0.05.
• The test statistic is computed as:
• Since it is hypothesized that the two methods are comparable and the difference between the means of the two populations is zero (µA - µB = 0), the calculation can be simplified as follows:
• If this t calc is greater than 1.96 then the null hypothesis of no difference can be overturned (p0.05).
• Even though we have used several different mathematical formulae, the interpretations are the same as before.

Directional hypothesis testing vs non-directional testing

Remember the example of testing the effect of antibiotics on mice in Lesson 7. The point of the study was to find out if the mice who were treated with the antibiotic would outlive those who were not treated (i.e., the control group). Are you surprised that the researcher did not hypothesize that the control group might outlive the treatment group? Would it make any difference in how the hypothesis testing were carried out? These questions raise the issue of directional testing, or one-tailed vs two-tailed tests.

The issue of two-side vs one-side tests becomes important when selecting the critical t-value. In the earlier discussion of this example, the alpha level was set to 0.05, but that 0.05 was actually divided equally between the left and right tails of the distribution curve. The condition being tested is that group A has a "different" life span as compared to group B, which represents a two-tailed test as illustrated in Figure 8-2.

Critical t-values for one-tailed and two tailed tests

This can be confusing and it should be helpful to think about having one or two gates on the curve. For example, for an alpha level of 0.05 and a two-tail test, there are two gates - one at -1.96 and one at +1.96. If you "walk" out of either of those gates, then you have demonstrated significance at p=0.05 or less. For a one-tail test, there is only one gate at +1.65. If you "walk" out of this gate, you have demonstrated significance at p=0.05 or less.

To reiterate, if you are standing right at the gate (1.96) for a two-tail test, then you have just barely met the p=0.05 requirement. However, if you are standing at the 1.96 point when running a one-tail test, then you have already exceeded the 1.65 gate and the probability must be even more significant, say p=0.025. It's important to find the critical t-value that is correct for the intended directional nature of the test.

About the author: Madelon F. Zady

Madelon F. Zady is an Assistant Professor at the University of Louisville, School of Allied Health Sciences Clinical Laboratory Science program and has over 30 years experience in teaching. She holds BS, MAT and EdD degrees from the University of Louisville, has taken other advanced course work from the School of Medicine and School of Education, and also advanced courses in statistics. She is a registered MT(ASCP) and a credentialed CLS(NCA) and has worked part-time as a bench technologist for 14 years. She is a member of the: American Society for Clinical Laboratory Science, Kentucky State Society for Clinical Laboratory Science, American Educational Research Association, and the National Science Teachers Association. Her teaching areas are clinical chemistry and statistics. Her research areas are metacognition and learning theory.

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• Toshiyasu Suzuki 2 , 3 ,
• Sebastian Ö. G. Pohl   ORCID: orcid.org/0000-0002-4294-9498 1 ,
• Nora J. Doleschall 1 ,
• Kevin Donnelly 1 ,
• Colin Nixon   ORCID: orcid.org/0000-0002-8085-2160 2 ,
• Mark J. Arends   ORCID: orcid.org/0000-0002-6826-8770 1 ,
• Shahida Din   ORCID: orcid.org/0000-0003-2855-3400 4 ,
• Kathryn Kirkwood 5 ,
• Jair Marques Junior   ORCID: orcid.org/0000-0002-8095-8475 1 ,
• Alex Von Kriegsheim   ORCID: orcid.org/0000-0002-4952-8573 1 ,
• Seth B. Coffelt   ORCID: orcid.org/0000-0003-2257-2862 2 , 3 &
• Kevin B. Myant   ORCID: orcid.org/0000-0001-8017-1093 1  

Oncogene ( 2024 ) Cite this article

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• Colorectal cancer
• Inflammation

Inflammatory Bowel Disease-associated colorectal cancer (IBD-CRC) is a known and serious complication of Inflammatory Bowel Disease (IBD) affecting the colon. However, relatively little is known about the pathogenesis of IBD-associated colorectal cancer in comparison with its sporadic cancer counterpart. Here, we investigated the function of Dock2 , a gene mutated in ~10% of IBD-associated colorectal cancers that encodes a guanine nucleotide exchange factor (GEF). Using a genetically engineered mouse model of IBD-CRC, we found that whole body loss of Dock2 increases tumourigenesis via immune dysregulation. Dock2 -deficient tumours displayed increased levels of IFNγ-associated genes, including the tryptophan metabolising, immune modulatory enzyme, IDO1, when compared to Dock2 -proficient tumours. This phenotype was driven by increased IFNγ-production in T cell populations, which infiltrated Dock2 -deficient tumours, promoting IDO1 expression in tumour epithelial cells. We show that IDO1 inhibition delays tumourigenesis in Dock2 knockout mice, and we confirm that this pathway is conserved across species as IDO1 expression is elevated in human IBD-CRC and in sporadic CRC cases with mutated DOCK2 . Together, these data demonstrate a previously unidentified tumour suppressive role of DOCK2 that limits IFNγ-induced IDO1 expression and cancer progression, opening potential new avenues for therapeutic intervention.

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Introduction.

Inflammatory Bowel Disease–associated colorectal cancer (IBD-CRC) is a long term colonic complication of inflammatory Bowel Disease (IBD) [ 1 ]. IBD is a complex, chronic disease affecting the gastrointestinal tract, characterised by immune dysregulation as a result of genetic, environmental and microbiotal factors [ 2 ], and is generally classified into two subtypes: Ulcerative Colitis, and Crohn’s Disease. IBD-CRC occurs on a background of colonic inflammation, and occurs in younger patients [ 3 ], and is associated with increased mortality compared to sporadic colorectal cancer [ 3 , 4 ], yet its pathogenesis is poorly understood. There is therefore an urgent need to understand IBD-CRC in greater detail.

The mutational spectrum of IBD-CRC is different from sporadic colorectal cancer with alterations in TP53 being the most common driver event and APC and KRAS being mutated at significantly lower rates [ 5 , 6 , 7 ]. In addition, alterations in the RHO/RAC signalling pathway are frequently associated with IBD-CRC with exome sequencing studies suggesting a prevalence of 50–70% of tumours having alterations in this pathway [ 6 , 7 ]. It should be noted however that these data do not imply significant mutation, and the functional effects of these mutations are unknown. RAC proteins have a critical role in several cellular processes including migration, apoptosis, proliferation, and invasion [ 8 , 9 , 10 , 11 ] but their role in the etiology of IBD-CRC is poorly understood.

Of particular interest are mutations in the dedicator of cytokinesis 2 ( DOCK2 ) gene, which have been found in ~10% of IBD-CRC cases [ 6 , 7 ]. DOCK2 is a 212 kDa protein expressed in both human and mouse, found mainly in peripheral blood cells, and to a lesser extent, the thymus and spleen [ 12 ]. DOCK proteins act primarily as guanine nucleotide exchange factors (GEFs) for the RAC/RHO GTPases [ 13 ]. GEF activity involves catalysing the release of GDP in exchange for GTP on RAC, resulting in RAC activation [ 11 ]. DOCK2 specifically is involved in both the innate and adaptive immune responses and plays a critical role in activation and proliferation of T lymphocytes [ 14 ]. DOCK2 was recently identified as a key driver gene in human IBD using network predictive modelling [ 15 ]. Dock2 —/— mice are also more sensitive to colitis as a result of Citrobacter rodentium infection [ 16 ]. As DOCK2 has been linked to both IBD and IBD-CRC in the context of its known physiological effect on immune cells, it is a particularly attractive target to study, and to our knowledge, the functional role of DOCK2 on the development of IBD-CRC has not been previously examined.

Here, we demonstrate loss of Dock2 in a Dextran Sodium Sulphate (DSS) colitis-induced mouse model of IBD-CRC increases tumour formation. We find this increased tumourigenesis is associated with CD3 + T cell infiltration, increased interferon gamma signalling, elevated expression of the IFNγ target IDO1 and increased tryptophan metabolism in Dock2 -deficient tumours. In addition, we show elevated production of IFNγ in immune cell populations, including γδ and CD8 T cells, under normal homeostatic conditions suggesting loss of Dock2 leads to immune cell dysfunction and aberrant IFNγ signalling. IDO1 is elevated in human IBD-CRC patients and pharmacological inhibition of IDO1 activity in Dock2- deficient mice abrogates tumourigenesis. Together, these results outline a novel functional role for aberrant IFNγ producing immune dysfunction in promoting IBD-CRC via modulation of IDO1-mediated tryptophan metabolism and suggest potential therapeutic avenues for targeting human disease.

Loss of Dock2 leads to increased tumour formation in vivo

To determine whether loss of Dock2 promotes inflammation-associated CRC development we crossed mice carrying a whole mouse ‘deletion first’ Dock2 tm1a allele to a well characterised villin-cre ERT2 Apc fl/+ intestinal CRC model [ 17 ] (Fig. S1A , B ) generating cohorts of control villin-cre ERT2 Apc fl/+ ( Vil Apc ) and experimental villin-cre ERT2 Apc fl/+ Dock2 tm1a/tm1a ( Vil Apc Dock2 ) mice. Following induction with tamoxifen, mice were subjected to two rounds of colitis-inducing DSS treatment, aged to 47 days where after colons were harvested for histological analysis (Fig. 1A ). Loss of Dock2 expression due to the ‘deletion first’ allele was confirmed in colonic tissue by mRNA and protein analysis and in thymus by RT-qPCR (Fig. S1C – E ). Vil Apc Dock2 mice developed more colonic tumours than controls and had an increased overall tumour burden (Fig. 1B–D ). Average tumour size and tumour cell proliferation was not different between groups (Fig. 1E–G ) suggesting increased tumourigenesis was not due to accelerated tumour growth (Fig. 1F, G ).

A Schematic detailing the Apc loss-mediated mouse model of IBD-associated colorectal cancer, including inducible gene modification with tamoxifen, two rounds of 0.5% DSS, and termination at day 47 post DSS administration. B Representative H&E-stained colonic Swiss rolls, detailing tumour burden in Vil Apc and Vil Apc Dock2 mice (black arrows). Scale bars are 2.5 mm. C Tumour number in Vil Apc and Vil Apc Dock2 mice. N = 11 vs 11 mice. D Total tumour burden in Vil Apc and Vil Apc Dock2 mice. N = 11 vs 11 mice. E Representative BrdU-stained Vil Apc and Vil Apc Dock2 tumours. Scale bars are 50 μm. F Quantification of BrdU staining. N = 7 vs 11 mice. G Average tumour size in Vil Apc and Vil Apc Dock2 mice. N = 11 vs 11 mice. Data represented as mean and error bars SD. All statistical analysis for this figure was performed using two-tailed Mann–Whitney test. Exact p values are indicated in the panels.

Tumours in mice lacking Dock2 show an immune phenotype, elevated interferon gamma signalling, and upregulation of IDO1

To identify potential molecular mechanisms mediating increased tumourigenesis following Dock2 deletion we carried out RNAseq on 5 vs 9 colonic tumours dissected from Vil Apc and Vil Apc Dock2 mice. This analysis revealed 80 transcripts with altered expression (FC > ±1.5, padj < 0.05; 65 upregulated, 15 downregulated) (Fig. 2A and Table S1 ). Dock2 was the most downregulated gene, with a logFC of −2.89 and an adjusted p value = 8.87 × 10 −35 , in line with the minimal Dock2 expression observed in the Vil Apc Dock2 tumours (Fig. S1C , D ). Gene ontology (GO) analysis of our dataset identified enrichment of numerous immune related pathways as being activated in Dock2 deficient tumours (Fig. 2B and Table S2 ). These included pathways involved in immune system processes, MHC protein binding and response to interferon signalling. Additionally, transcription factor binding site (TFBS) analysis demonstrated an enrichment of putative targets of interferon regulatory factors (IRFs) (Fig. 2B and Table S2 ). Furthermore, gene set enrichment analysis (GSEA) identified enrichment of interferon gamma and alpha response hallmark gene sets with genes upregulated in our dataset (Fig. S2A ; IFNγ p < 0.001 and IFNα p = 0.005). Together, this suggests loss of Dock2 leads to dysregulation of immune system processes and, in particular, activation of interferon signalling. Quantitative RT-PCR was used to validate a selection of the identified interferon related genes with all tested genes showing the expected transcriptional changes (Fig. 2C ). One of the most upregulated genes following Dock2 loss was indoleamine 2, 3-dioxygenase 1 ( Ido1 ). Ido1 is a previously described target of IFNγ signalling with known tumour promoting, immunomodulatory functions [ 18 , 19 ]. Therefore, we chose to investigate IDO1 in more detail. We further validated this upregulation at protein level observing upregulation of IDO1 protein in Dock2 deficient tumours by IHC and Western blot (Fig. 2D, E and Fig. S2B , C ). To define in better detail the cellular compartment expressing IDO1 we co-stained Vil Apc Dock2 tumour tissue for epithelial and immune cell markers alongside IDO1. IDO1 expression was exclusively co-localised with CDH1 expression and we did not observe co-expression with CD3 or CD8 markers (Fig. S2D , E ). Together, this suggests an induction of IFNγ signalling in the tumour epithelium of Dock2 deficient mice. We also observed increased Ido1 but not Ifng expression in normal tissue adjacent to tumours demonstrating this effect is not restricted to tumour cells (Fig. S2F ).

A Heatmap of genes with significantly altered expression in Vil Apc Dock2 tumour identified by RNAseq. B Gene ontology analysis of Vil Apc vs Vil Apc Dock2 tumours. The top 5 non-redundant Biological Processes (BP), Molecular Functions (MF), Cellular Components (CC) and Putative Transcription Factor Binding Sites (TFBS) are listed and corresponding –log10 padj shown. C RT-qPCR analysis of IFNγ target gene expression in Vil Apc and Vil Apc Dock2 tumours. N = 5 vs 6 tumours ( Ifng ) or 5 vs 9 tumours (others). D Representative IDO1-stained Vil Apc and Vil Apc Dock2 tumours. Scale bars are 500 μm and 100 μm (zoom). E Quantification of IDO1 staining. N = 6 vs 9 tumours. Data represented as mean and error bars SD. All statistical analysis for this figure was performed using two-tailed Mann–Whitney test. Exact p values are indicated in the panels.

To further investigate this, we determined whether colonic tumour epithelial cells respond to exogenous IFNγ. Colonic organoids were derived from control and Dock2 tm1a/tm1a ( Dock2 ) mice and Apc deleted using CRISPR to mimic tumour development in an inflammation naïve setting (Fig. S3A ). Apc KO and Apc KO Dock2 organoids were treated with mouse IFNγ for 24 h. There were no changes in organoid growth (Fig. 3A, B ) but we observed an upregulation of genes identified in our RNAseq dataset confirming they are IFNγ targets in colonic epithelial cells and indicating a key role for type II interferon signalling in the epithelial response to interferon stimulation (Fig. 3C ). This included increased Ido1 expression which was also observed at protein level (Fig. 3D–H ). Interestingly, whilst there was a modest increase in the activation of some IFNγ target genes in IFNγ treated Apc KO Dock2 organoids compared to IFNγ treated controls this was not the case for Ido1 mRNA or protein, the activation of which was broadly the same (Fig. 3C–E ). This was in contrast to our in vivo data showing an upregulation of Ido1 in Dock2 deficient mice. Thus, the responsiveness of Dock2 deficient and normal tumour organoids to IFNγ stimulation is broadly similar. Together, this suggests the increased IFNγ response observed in vivo may be due to elevated infiltration of IFNγ producing cells in Dock2 deficient tumours. To further test this, we analysed the dose dependency of Apc KO organoids to IFNγ stimulation. We found that IDO1 protein and mRNA expression was induced in a dose dependent manner by increasing concentrations of IFNγ (Fig. S3B – D ). Additionally, we found that Apc KO organoids did not express detectable levels of Ifng mRNA, even following inflammatory stimulus with TNFα. Together, this supports the hypothesis that increased IFNγ production, from non-cell autonomous sources, is driving elevated IDO1 expression in tumour epithelial cells in Dock2 deficient mice. Therefore, we sought to identify potential sources of this IFNγ production.

A Collage overview of Apc KO and Apc KO Dock2 organoids. B Quantification of Apc KO and Apc KO Dock2 organoid size change 24 h post IFNγ treatment. N = 30 vs 30 vs 30 vs 30 organoids. C RT-qPCR analysis of IFNγ target gene expression in Apc KO and Apc KO Dock2 organoids 24 h post IFNγ treatment. N = 3 vs 3 vs 3 vs 3 independent technical replicates and 3 vs 3 vs 2 vs 3 independent technical replicates ( Tap1 ). D Western blot analysis of IDO1 expression in Apc KO and Apc KO Dock2 organoids 24 h post IFNγ treatment. E Quantification of IDO1 Western blot. N = 3 vs 3 vs 3 vs 3 independent technical replicates. F Immunofluorescence staining of Ido1 expression in Apc KO organoids 24 h post IFNγ treatment. G Quantification of Ido1 staining intensity. N = 572 vs 232 organoid cells. H Quantification of % Ido1 positive cells. N = 3 vs 3 independent technical replicates. Data represented as mean and error bars SD. Statistical analysis for ( B ), ( C ) and ( E ) was performed using ordinary one-way ANOVA with Tukey’s multiple comparisons. Statistical analysis for ( G ) and ( H ) was performed using student T-test *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Tumours in mice lacking Dock2 are infiltrated by CD3+ and γδ T lymphocytes

As DOCK2 primarily functions as an immune modulator we examined whether immune cell dysregulation might be responsible for the observed IFNγ stimulation and thus impacting tumour formation in Dock2 deleted mice. We first investigated lymphoid and myeloid cell populations using immunohistochemistry and found an increase in intratumoural CD3 + T cell infiltration in Vil Apc Dock2 tumours (Fig. 4A, B ) but not of macrophages or neutrophils (Fig. S4A , B ). Further investigations of different T cell subtypes demonstrated that whilst CD4+ and CD8 + T cell infiltration was the same between groups (Fig. S4C , D ), there were more γδ T cells in Vil Apc Dock2 tumours than control tumours (Fig. 4C, D ). The expression of Trgv1 , the gene encoding T cell receptor Vγ1 chain, but not Trgv4, Trgv5, Trgv6 or Trgv7 was also upregulated in tumours of Vil Apc Dock2 mice (Fig. 4E and S4E ). Infiltrating Vγ1 γδ T cells have previously been identified as a potential source of IFNγ in tumours suggesting this immune cell population may be partly mediating IFNγ signalling in Dock2 deficient tumours.

A Representative CD3-stained Vil Apc and Vil Apc Dock2 tumours. Scale bars are 50 μm ( B ) Quantification of CD3 staining. Values represent % of total tumour cells. N = 6 vs 10 mice. C Representative images of RNAscope for TRDC in tumours of Vil Apc and Vil Apc Dock2 mice. Scale bars are 100 μm. D Quantification of TRDC staining. Values represent % of total tumour cells. N = 7 vs 20 tumours. E RT-qPCR analysis of Trgv1 expression in Vil Apc and Vil Apc Dock2 tumours. N = 5 vs 9 tumours. Data represented as mean and error bars SD. All statistical analysis for this figure was performed using two-tailed Mann–Whitney test. Exact p values are indicated in the panels.

γδ T cells from mice lacking Dock2 produce more IFNγ in vivo

We next examined in more detail whether γδ T-cells are a source of IFNγ in our Dock2- deficient mouse model and whether this phenotype is intrinsic to Dock2 loss or requires exogenous inflammatory stimulus. To address this, we analysed the effects of Dock2 deletion on colonic immune cell populations under both homeostatic and inflammatory conditions. Acute colonic damage and inflammation was induced by treatment with 2% DSS. Monitoring of mice during treatment indicated no significant differences in disease severity (as indicated by weight loss) between control and Dock2 deficient mice (Fig. 5A ). After 7 days treatment (5 days DSS + 2 days normal drinking water) mice were culled and colons harvested (Fig. S5A ). Histological analysis indicated no significant difference in the extent of mucosal damage or CD3 + T cell infiltration between control and Dock2 deficient mice (Figs. 5B, C and S5 B, C ) demonstrating loss of Dock2 does not increase susceptibility to DSS induced colitis. To investigate the potential for immune cell dysregulation during acute colitis in more detail we carried out flow cytometry analysis of different lymphoid populations (Fig. S5D ). Consistent with our previous findings in cancer models, we found γδ T cells were more abundant in Dock2 deficient than control colons (Fig. 5D, E ). Notably, this increase was found under normal, homoeostatic conditions in the absence of DSS induced inflammatory stimulus. Indeed, upon DSS treatment, the proportion of γδ T cells was not different between control and Dock2 deficient mice (Fig. 5D, E ). We next determined whether γδ T cells could act as a source of IFNγ explaining the increased IFNγ activation observed in Dock2 deficient mice. Flow cytometry analysis showed that a higher proportion of both γδ T cells and CD8 + T cells expressed IFNγ in Dock2 deficient colons. Again, this increase was present in the absence of DSS treatment, suggesting Dock2 loss leads to an intrinsic immune cell dysfunction manifested by increased infiltration of IFNγ producing γδ T cells (Fig. 5F–I ). To determine whether other immune populations could contribute to increased IFNγ production we further analysed IFNγ levels in CD3 positive and CD3 negative cells by flow cytometry. We found that the majority of IFNγ producing cells were CD3 positive but IFNγ was also produced by CD3 negative cells and the proportion of these cells was also significantly higher in Dock2 deficient mice (Fig. S5F , G ). Together, this suggests Dock loss leads to immune dysregulation, characterised by increased IFNγ production in multiple immune subtypes including, but not restricted to, γδ T cells and CD8 + T cells.

A Comparative weights of WT and Dock2 mice during treatment with 2% DSS. B Representative H&E-stained colonic Swiss rolls, detailing extent of colitis in WT and Dock2 mice. Representative areas of epithelial erosion are highlighted. Scale bars are 2.5 mm and 100 μm (zoom). C Quantification of colitic area in WT and Dock2 mice. N = 17 vs 14 mice. D Representative plots of CD3+ γδ T cells in WT and Dock2 colons at baseline or following acute DSS treatment. E Quantification of CD3+ γδ T cells. N = 3 vs 3 vs 3 vs 3 mice. F Representative plots of IFNγ producing γδ T cells in WT and Dock2 colons at baseline or following acute DSS treatment. G Quantification of IFNγ producing γδ T cells. N = 3 vs 3 vs 3 vs 3 mice. H Representative plots of IFNγ producing CD8 + T cells in WT and Dock2 colons at baseline or following acute DSS treatment. I Quantification of IFNγ producing CD8 + T cells. N = 3 vs 3 vs 3 vs 3 mice. J RT-qPCR analysis of IFNγ target gene expression in WT and Dock2 colons at baseline or following acute DSS treatment. N = 6 vs 6 vs 17 vs 14 mice. Data represented as mean and error bars SD. Statistical analysis for ( C ) was performed using two-tailed Mann–Whitney test. Statistical analysis for ( E ), ( G ), ( I ) and ( J ) was performed using ordinary one-way ANOVA with Tukey’s multiple comparisons. Exact p values are indicated in the panels.

To determine whether this led to increased IFNγ target gene expression in the colon we carried out transcriptional analysis of previously identified IFNγ responsive genes. Under homeostatic conditions Dock2 loss was not sufficient to increase IFNγ signalling in the colon but following DSS treatment a number of IFNγ responsive genes were induced (Fig. 5J ). Therefore, Dock2 deficient colonic epithelium is not inherently IFNγ activated, rather, it responds to inflammation to induce and maintain this state. To investigate more broadly, we also examined expression of γδ T cell markers, and some IFNγ responsive genes in thymus from untreated control and Dock2 deficient mice. These mice exhibit significantly higher expression of various γδ T cell markers but not IFNγ target genes (Fig. S5H ). Together, these data suggest Dock2 deficient mice have increased infiltration of IFNγ-producing immune cells, but exogenous inflammatory stimuli are needed to induce robust IFNγ activation and IDO1 expression in the colonic epithelium.

IDO1 expression is increased in human IBD-CRC and IDO1 inhibition abrogates tumourigenesis in Dock2 deficient mice

To determine whether the observations from our model are recapitulated in human disease we investigated the expression of the IFNγ target IDO1 in a set of human samples encompassing different stages of IBD-CRC disease progression. IHC analysis showed that IDO1 expression is significantly increased in inflamed tissue compared to normal colon and this increased expression persists through to the development of cancer (Fig. 6A, B ).

A Representative IDO1-stained human normal colon, inflamed colon, dysplastic colon and IBD associated cancer (all regions of the same human resection specimen). Scale bars are 250 μm and 50 μm (zoom) ( B ) Quantification of IDO1 staining. N = 5 vs 5 vs 4 vs 12 patient samples. C Expression of IDO1 and IFNG in CRC patients with wildtype or mutated DOCK2 . D Expression of IDO1 and IFNG in MSS CRC patients with wildtype or mutated DOCK2 . E Survival plot of TCGA CRC dataset grouped on the mutational status of DOCK2 . Note reduced survival of DOCK2 mutant patients. F Schematic outlining the tryptophan metabolism pathway. Note IDO1 catalysers the conversion of tryptophan to kynurenine. Metabolites altered in Vil Apc Dock2 tumours are highlighted in blue if downregulated (tryptophan) or red if upregulated (xanthurenate). G Volcano plot showing the polar metabolites filtered using p > 0.05 and FC > 1.3. H Individual value plots of log 2 intensity values of selected metabolites. N = 12 vs 12 tumours. Data represented as mean and error bars SD. Statistical analysis for ( B ) was performed using ordinary one-way ANOVA with Tukey’s multiple comparisons. Statistical analysis for ( C ) and ( D ) was performed using student T-test. Statistical analysis for ( H ) was performed using two-tailed Mann–Whitney test. Exact p values are indicated in the panels.

To extend these analyses we utilised the TCGA dataset of sporadic CRC. DOCK2 is mutated in ~10% of cases and in samples where DOCK2 is mutated, expression of both IFNG and IDO1 was significantly higher than non-mutated samples (Fig. 5C ). Importantly, this is also observed in microsatellite stable (MSS) CRC which is generally immune suppressed (Fig. 5D ). We also found that DOCK2 mutation and/or low DOCK2 expression correlates poor prognosis in these patients, further supporting a tumour suppressor role for this protein (Figs. 6E and S6 A, B ).

IDO1 is a tryptophan catabolic enzyme that catalysers the conversion of tryptophan to kynurenine and other downstream metabolites (Fig. 6F ). This pathway plays an important immunomodulatory role by regulating the activity of both regulatory and effector T cells [ 20 , 21 , 22 ]. We next investigated whether the increased IDO1 observed in Dock2 deficient tumours impacted on tryptophan metabolism. To identify metabolic differences between Vil Apc and Vil Apc Dock2 tumours, we performed liquid chromatography mass spectrometry (LC-MS). We extracted metabolites using a solid-liquid biphasic extraction [ 23 ] from 12 tumours of each group and analysed the aqueous fraction by high-resolution HILIC LC-MS [ 24 ]. We identified numerous changes in metabolite levels in Dock2 deficient tumours (Figs. 6G , S6 C, S6D and Table S3 ). Consistent with the elevated expression of IDO1, we observed a depletion of tryptophan following Dock2 deletion (Fig. 6G, H ). Interestingly, we did not observe differences in the levels of kynurenine but the downstream metabolite xanthurenate was elevated in Vil Apc Dock2 tumours suggesting a rapid conversion of kynurenine via this pathway (Fig. 6F–H ).

To further test the functional significance of elevated IDO1 expression and tryptophan metabolism in Dock2 deficient tumours we next examined the effect of IDO1 inhibition on tumourigenesis. We treated Vil Apc Dock2 mice with the IDO1 inhibitor 1-L-MT whilst undergoing repeated rounds of DSS induced colitis (Fig. 7A ). Mice were culled at 47 days post first day of DSS and tissue harvested for analysis. Consistent with a functional role for IDO1 expression in driving IBD-CRC development, mice treated with 1-L-MT had a significantly lower tumour number and tumour burden compared to mice treated with vehicle (Figs. 7B–D and S7A ). Importantly, this was not due to IDO1 inhibition impacting on severity of colitis, confirming the tumour promoting effects of increased epithelial IDO1 activity following Dock2 deletion (Fig. S7B ). Together, our data implicate Dock2 loss of function in the development of IBD-CRC. Mechanistically, this is via immune dysregulation leading to increased infiltration of IFNγ producing T cells, driving IDO1 expression.

A Schematic outlining IDO inhibitor (1-L-MT) treatment regimen in Vil Apc Dock2 mice. B Representative H&E-stained colonic swiss rolls, detailing tumour burden in vehicle and IDO1 inhibitor (1-L-MT) treated Vil Apc Dock2 mice (black arrows). Scale bars are 2.5 mm. C Tumour number in vehicle and IDO1 inhibitor treated Vil Apc Dock2 mice. D Total tumour burden in vehicle and IDO1 inhibitor treated Vil Apc Dock2 mice. N = 8 vs 8 mice. E A model outlining the proposed role of Dock2 in suppressing inflammation induced colorectal cancer. Data represented as mean and error bars SD. All statistical analysis for this figure was performed using two-tailed Mann–Whitney test. Exact p values are indicated in the panels.

Taken together, these results suggest that loss of Dock2 leads to immune dysregulation, enhancing tumourigenesis via interferon gamma induced expression of IDO1. In mice lacking Dock2 , IFNγ production is increased in multiple T cell populations, including CD8+ and γδ T cells, and administrating IFNγ to tumour organoids induces robust IDO1 expression suggesting this response is driven by immune cell IFNγ production. Finally, treatment with 1-L-MT, an IDO1 inhibitor, abrogates the increased tumourigenesis observed in mice without Dock2 .

IDO1 is an enzyme catalysing the conversion of tryptophan to kynurenine [ 25 ] and has been directly associated with tumourigenesis, as IDO1+ Paneth cells promote immune evasion in sporadic colorectal cancer [ 26 ]. In colitis-associated cancer, conditional loss of IDO1 in the colonic epithelium with AOM/DSS resulted in fewer colonic tumours [ 18 ]. Additionally, 1-L-MT, an inhibitor of IDO1, decreases proliferation of colon cancer cell lines [ 27 ], as well as reduces tumour burden in AOM/DSS mice [ 27 ]. IDO1 −/− mice treated with AOM/DSS have separately been shown to develop smaller tumours [ 28 ]. We also observed decreased tumourigenesis in a colitis induced Dock2 deficient tumour model following 1-L-MT treatment, outlining the importance of tryptophan metabolism in modulating colitis-induced CRC. IDO1 is associated with a reduction in immune system activity [ 25 ] through several mechanisms. First, macrophages [ 22 ] and dendritic cells [ 21 ] expressing IDO1 suppress T cell proliferation via decreasing the tryptophan pool. Second, dendritic cells expressing IDO1 expand the regulatory T cell (Treg) population thus modulating inflammatory activity [ 20 ]. IDO1 also mediates increased tumourigenesis independently of its effect on T cells, as tumourigenesis was also impaired in IDO1 knock out, immune deficient Rag1 -knockout mice [ 29 ]. Although IDO1 is not abundantly expressed in the colonic epithelium at baseline [ 19 , 30 ], it is expressed in the epithelium during active IBD [ 19 ] consistent with the findings of our study. Interestingly, when overexpressed in the epithelium it results in increased secretory cell differentiation and larger mucus layer in the ileum, as well as reduced sensitivity to DSS [ 31 ]. Along these lines, induction of IDO1 affects colitis severity [ 32 ]. However, the literature varies with respect to IDO1 loss or inhibition, with both positive and negative results reported in colitis: IDO1-deficient mice are less sensitive to DSS colitis [ 33 ], yet mice treated with both IDO1 stimulation and IDO1 inhibitor combined, lose the protective effect of IDO1 stimulation [ 32 ]. Thus, activation of IDO1 activity has the potential to mediate colonic homeostasis and tumourigenesis via multiple mechanisms.

Our data strongly implicate IFNγ signalling in driving expression of IDO1, indeed we observed a robust, dose dependent, transcriptional activation of IDO1 following treatment of colonic organoids with recombinant IFNγ. Subsequent analyses identified increased infiltration of CD3 + , CD8+ and γδ T cells as a source of IFNγ in Dock2 deficient tissue although we cannot rule out other immune populations also playing a role. It is not clear why more IFNγ producing T cells accumulate in tumours formed in Dock2 mutant mice, a gene that is expressed primarily in hematopoietic cells. These cells may be recruited to tumours by increased expression of chemokines or they may proliferate in situ. Neither is it clear why a predominance of IFNγ-producing T cells, normally associated with anti-tumour activity [ 34 , 35 , 36 ], correlate with increased tumourigenesis. For example, Vγ1 γδ T cells play a role in counteracting tumour cell survival through communication with intraepithelial lymphocytes expressing the Vγ7 TCR [ 37 ]. Additionally, IFNγ producing CD8 + T cells are known mediators of anti-tumour immunity [ 38 ]. On the other hand, IFNγ can contribute to immune cell evasion, in particular via modulation of IDO1 expression [ 38 ]. Given that T cells lack Dock2 expression in the model used here, it is tempting to speculate that the absence of DOCK2 suppresses their cancer-killing functions via preventing synapse formation with target cancer cells, a previously described function of DOCK2 [ 39 ]. This defective recognition of cancer cells together with the upregulation of IFNγ in DOCK-2 deficient T cells may result in tumour promotion via increased expression of IDO1. Anti-tumourigenic effects of IDO1, such as induction of T cell apoptosis [ 40 ], were not observed either, arguing against immune escape by T cell ablation. It is reasonable, therefore, to suggest that these increased Dock2 deficient IFNγ producing T cells are dysfunctional. Thus, despite accumulating in the colon they are not sufficient to induce an anti-tumour microenvironment, rather via induction of IDO1 expression, promote one that supports tumour development.

Despite these open questions it appears clear that Dock2 deficiency leads to increased tumourigenesis in an IDO1 dependent manner. Additionally, we found increased levels of IDO1 in IBD-CRC samples and in patients with sporadic CRC carrying mutations in the DOCK2 gene. Furthermore, DOCK2 mutational status was correlated with poor survival outcomes in sporadic CRC. Our identification of this pathway, and its relevance in human disease, could therefore help inform the clinical management of IBD-CRC. With regards to this, it is important first to note the status of IDO1 inhibitors in recent clinical trials. Several trials across multiple solid tumour types have revealed disappointing results [ 41 ]. However, these trials have not selected patients based on IDO1 tumour positivity, and none of these trials have occurred in patients with colorectal cancer, particularly following inflammation. Therefore, it remains to be seen whether IDO1 inhibition has a role in the treatment of inflammation-associated colorectal cancer, and this potential needs to be fully explored. Alternatively, this poor performance may indicate that IDO1 activity is not required to fuel tumour growth in established cancers. In this case, IDO1 inhibitors may be more efficacious when used as chemopreventative agents, preventing the initiation of tumourigenesis. It is worth noting that in our model, we inhibited IDO1 during DSS treatment, suggesting a key role during colonic damage and regeneration cycles. Therefore, IDO1 inhibitors may be particularly beneficial to patients with IBD and at high risk of developing cancer, in particular those carrying previously identified high risk DOCK2 mutations [ 15 ]. Additionally, IDO1 activity, identified through increased xanthurenate, has been shown to negatively correlate with intestinal inflammation itself, and modification upstream of xanthurenate has protected against murine colitis [ 41 ]. Therefore, there are several opportunities for biomarker identification, as well as potential therapeutic agents, in both inflammation and inflammation-associated cancer.

To summarise, we have identified an IDO1 induced tryptophan metabolic pathway regulated by IFNγ producing immune cells driving IBD-CRC, highlighting the intricate role of the immune and interferon gamma response in IBD-CRC (Fig. 7E ). This enhances our understanding of the etiology of IBD-CRC and identifies these processes as potential therapeutic targets for this complex and chronic disease.

Materials and methods

Animal models, mus musculus.

All animal experiments were performed in accordance with a UK Home Office license (Project License 70/8885), and were subject to review by the animal welfare and ethics board of the University of Edinburgh. Mice of both genders were used at an age of 6–12 weeks. Mice were bred at the animal facilities of the University of Edinburgh and were kept in 12 h light–dark cycles and were given access to water and food ad libitum. Mice were maintained in a temperature- (20–26 °C) and humidity- (30–70%) controlled environment. Colonies had a mixed background (50% C57Bl6J, 50% S129). The genetic alleles used for this study were as follows: villin-cre ERT2 [ 42 ], Apc fl [ 43 ] and Dock2 tm1a (EUCOMM). The Dock2 tm1a allele is generated by insertion of a lacZ and neo containing construct into Dock2 gene. Insertion of this construct disrupts gene expression, resulting in a ‘knockout-first’ allele. Therefore, the Dock2 tm1a allele is Dock2 knockout in all mouse tissues, including immune cell populations. Mice were genotyped by Transnetyx (Cordoba, USA). At experiment endpoints, mice were humanely sacrificed by cervical dislocation (CD) in line with UK Home Office regulations.

Animal experiments

Gene deletion in villin-cre ERT2 Apc fl and villin-cre ERT2 Apc fl Dock2 tm1a mice was induced as previously described using intraperitoneal tamoxifen at 80 mg/kg [ 44 ]. Chemical colitis in experimental animals was triggered using DSS (36,000–50,000 Da, MP Biomedicals), reconstituted in distilled water. Mice were treated with two 7-day cycles of 0.5% DSS, with recovery in between, and aged. Labelling of actively replicating cells was achieved through IP injection of 200 μl of BrdU (Amersham Bioscience), 1–2 h prior to Schedule 1 culling. For the inhibitor experiment, mice were treated with 400 mg/kg 1-LMT, an IDO1 inhibitor, daily for the period of DSS administrations, plus two further days via gavage, or vehicle (0.5% methylcellulose/0.5% Tween 80) without drug. Power calculations based on previous results from similar experiments were carried out prior to the experiment to ensure appropriate sample sizes to determine statistically significant effects. Investigators were blinded to mouse genotype during experiment and data analysis.

Patient samples

Anonymised archival paraffin embedded human colonic resection specimens from patients with IBD-associated colorectal cancer held within the Lothian NRS Bioresource were obtained following approval (Sample Requests SR1165 and SR1165- AM01 301120; Ethical Approvals 15/ES/0094 and 20/ES/0061). Informed consent was obtained from all subjects. Relevant areas of normal tissue, inflammation, dysplasia and cancer were identified by a pathologist and samples were used for subsequent immunohistochemical analysis.

Immunohistochemistry

After dewaxing, and where necessary, methacarn-fixed slides were treated with 4% PFA for 10 min. Antigen retrieval at 99 °C was in one of two buffers according to protocol: citrate (pH 6) or EDTA. Hydrogen peroxide was used to prevent endogenous staining at either 1.5% or 3%. Sections then were blocked in 5% goat serum (Sigma) for an hour, before application of the relevant primary antibody at dilutions in 5% goat serum, overnight at 4 °C. The following day the secondary antibody (DAKO Envision) was applied according to manufacturer’s recommendations. Staining for all sections was developed using diaminobenzidine (Thermo Scientific) for three minutes, before counterstaining and mounting. BrdU (BD Transduction (347580)) staining was at 1:500, using a citrate buffer for 25 min heating/30 min cooling, and 10 min hydrogen peroxide 1.5%. CD3 (DAKO (A045229)) was at 1:500, using a EDTA buffer for 15 min heating/30 min cooling, and 10 min hydrogen peroxide 3%. IDO1 (Cell Signalling (51851S)) staining was at 1:100, using a citrate buffer for 20 min heating and 20 min cooling, and 10 min hydrogen peroxide 3%.

Primers used for the amplification of each selected gene are detailed in Table S4 . The reaction used SYBR Select Master Mix (Applied Biosystems), together with forward and reverse primer (combined and diluted 1:10), and water. Gene expression levels were examined in duplicate and normalised to Actb using 2^ -(ΔΔ Cycle threshold) calculations. The protocol involved incubation at 95 °C for 15 min, and then 45 cycles of [denaturation at 95 °C for 10 s, annealing at 60 °C for 30 s, and extension at 72 °C for 30 s]. Primers used for analysis are listed in Table S4 .

RNASeq on individual, isolated tumours was carried out by the Welcome Trust Clinical Research Facility (WTCRF) at the Western General Hospital, Edinburgh. After RNA extraction, samples were checked for RNA quality using a Bioanalyser - all had a RIN of 9.1 or above. Libraries were prepared using 500 ng of each sample with the NEBNEXT Ultra II Directional RNA Library Prep kit (NEB #E7760) and the Poly-A mRNA magnetic isolation module (NEB #E7490). Sequencing was performed using the NextSeq 500/550 High-Output v2.5 Kit (#20024907) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002). All libraries generated greater than 24 million paired end reads. FASTQ files (four lanes per sample) were subsequently uploaded for analysis using DESeq2. Significant genes were considered for pathway analyses using G:profiler ( https://biit.cs.ut.ee/gprofiler/ ). Genes were pre-ranked and analysed for Gene Set Enrichment Analysis (GSEA, v3.0) to generate enrichment plots for Hallmark datasets.

In-situ hybridisation (ISH) staining was performed on 4 µm formalin fixed paraffin embedded sections (FFPE) which had previously been ovened at 60 °C for 2 h. ISH detection for, mRNA probe for Trdc (449358; Advanced Cell Diagnostics) was performed using RNAScope 2.5 LSx (Brown) detection kit (322700; Advanced Cell Diagnostics) on a Leica Bond Rx autostainer strictly according to the manufacturer’s instructions. To complete ISH staining sections were rinsed in tap water, dehydrated through graded ethanols and placed in xylene. The stained sections were coverslipped in xylene using DPX mountant (CellPath, UK).

Western blotting

Protein samples were first denatured in NuPAGE LDS sample loading buffer (ThermoFisher Scientific) together with NuPAGE sample reducing reagent at 99 °C for 5 min, before loading on to a gel. Gels used were SDS (NuPAGE) pre-prepared and were either 4–12% Bis-Tris or 3–8% Tris-Acetate, depending on the size of the protein of interest. Once running was complete, proteins were transferred onto a nylon membrane (PVDF, Amersham) in Nupage transfer buffer containing 20% methanol in ddH 2 0. Membranes were then blocked in 5% milk before applying antibody (IDO1 - Cell Signalling (51851S), or Vinculin - Abcam (ab73412) at 1:1000 overnight at 4 °C. Samples were then washed, before applying secondary antibody at a concentration of 1:5000 for an hour at room temperature, before developing using Pierce ECL (ThermoFisher) kits.

Flow cytometry

1 cm of colon was freshly dissected, 2 cm from the rectum, and digested in a cocktail of 2.5 ml RPMI media (Gibco) containing 5% FCS (in-house) 0.5 mg/ml collagenase (Sigma), 0.5 mg/ml DispaseII (Merck) and 3 mg/ml DNAseI (Roche) in a rocking incubator at 37 °C for 45 min before passing through a 70 μm strainer. 5 × 10 5 cells were plated per sample, with the remainder being used for unstained control. 150 μl of T cell stimulation media (IMDM, Sigma, plus β-mercaptoethanol 50 μM plus Pen-strep (in-house) +T cell stimulation cocktail (1:500, Invitrogen) was added to each well before placing the plate in the 37 °C incubator for three hours. Each stained well was then resuspended in 50 μl blocking buffer (50 μl FACS buffer and 1 μl fc block (TruStain FcX, Biolegend) for 20 min at 4 °C. Meanwhile an antibody mix was prepared using 1 μl per sample of the relevant extracellular antibody diluted 1:50 in Brilliant Stain buffer (BD Biosciences). 50 μl of antibody mix was added to each sample and the plate was further left at 4 °C for 30 min. 100 μl of Zombie live/dead agent (Biolegend) was added at 1:100 in cold PBS and again incubated for 20 min at 4 °C. Samples were resuspended in 75 μl fixation buffer (Invitrogen), again for 20 min at 4 °C. After incubation, 75 μl 1x permeabilisation buffer (Invitrogen, diluted in distilled water) was added before finally resuspending in FACS buffer and keeping at 4 °C overnight.

The following day 100 μl of intracellular antibody mix (diluted 1:100 in permeabilisation buffer) was added to each sample and the plate was incubated at 4 °C for 30 min. After intracellular antibody incubation, 50 μl permeabilisation buffer was added to each well, before resuspending each well in 100 μl FACS buffer. Samples were then flowed on BD Fortessa™. Antibodies used were Zombie Live/Dead (Biolegend (423107) – UV), CD3 (Biolegend (100219) – PE/Cy7), TCRγδ (Biolegend (118127) – AF 488) and IFNγ (Biolegend (505816) – AF647).

Organoid generation and culture

Media for organoid culture used Advanced DMEM/F12 (ADF - Gibco) with 5 mls HEPES (Invitrogen), 5 ml glutamine (in-house) and 5 ml Penstrep (in-house) added. Colonic Wild-type and Dock2tm1a/tm1a organoids were generated directly from epithelial tissue culture as part of this project. Colon was harvested and 25 mM EDTA was added for thirty minutes to catch free magnesium, loosening up the crypts from the surrounding tissue. Once the EDTA reaction was terminated the post-washing supernatant was collected and centrifuged and the end pellet was resuspended in ADF and passed through a 70 μm cell strainer before resuspending in BME and plating. Complete media containing 15% R-spondin-conditioned media (in-house – HEK-293 cells stably expressing HA-R-Spondin), 50 ng/ml EGF, 1X B27 (Life Technologies), 1X N2 (Life Technologies), 1% Noggin-conditioned media (in-house - HEK 293 cells stably expressing Noggin), 50% Wnt3a condition media (in-house –L-cells stably transfected expressing Wnt3a) and 3 μM StemMACS (CHIR99021, Abcam) was used in culture. Established organoids were then passaged a week later. Organoids were treated with mouse interferon-γ (Life Technologies), diluted in media at 1 ng/ml. Protein and RNA were collected form organoids at 24 h after administration of IFNγ, to compare to organoids cultured only in normal media.

Organoid CRISPR

Colonic wild-type and Dock2 tm1a/tm1a organoids were further modified to CRISPR Apc , rendering them Apc −/− and Apc −/− Dock2 tm1a/tm1a . On day 0 cells were split and plated in complete media. On day three they were again split and plated, with the StemMACS concentration raised, now being at 0.6 μl/ml. Additional factors added were Jagged (1 μM, Eurogentec), ROCK inhibitor (10 μM Y-27632, Tocris), and 1 mM VPA (Sigma). On day five viral infection took place, adding 1 ml Accutase™ (Life Technologies) and 4 μl Y-27632 to the harvested pellet. This reaction took place in a water bath at 37 °C for 3 min before termination of the enzymatic reaction with 1 ml 1% BSA and washing in ADF. 5 × 10 5 cells were plated per well of a six well plate with virus at an MOI of 50. Two guide RNAs for Apc , “APC1” (GGATCTGTATCCAGCCGTTC) and “APC3” (AGATCCTTCCCGACTTCCGT), were used. Media for transduction contained EGF, Noggin, R-spondin and Wnt as above, StemMACS 0.6 μl/ml, 1 μM Jagged, 10 μM Y-27632, 1 mM VPA (1:10000), and polybrene (8 μg/ml, Sigma). 24 h later, the infection media was removed and BME overlaid, with fresh media used (containing all constituents of the transduction media bar the polybrene). Media was changed again 24 h after that, this time replacing with ENRW with StemMACS at 1:200 and Y-27632 10 μM only. Media changes were then carried out as necessary until day 11 when growing organoids were split and R-spondin, Wnt and StemMACS removed.

Metabolomics

Polar metabolites were extracted using biphasic extraction. On a 1.5 mL Eppendorf vial, 100 µL of methanol containing internal standard (L-Glutamine_13C5,15N2) was added to the tissue samples, sonicated on ice cold bath for 10 min, following by the addition of 300 uL of MTBE. Vials were shacked for 20 min at 8 °C and 100 µL H2O was added to induce phase separation, followed by vertexing it for one minute and centrifuged at max speed for 10 min at 4 °C. The upper phase containing lipids and the lower phase containing the polar metabolites were individually transferred for new vials. The polar phase was placed at −80 °C for one hour to guarantee protein precipitation, centrifuged at max speed for 10 min at 4 °C and transfer to a 96 well plate for HPLC analysis. Samples were analysed on a Dionex UltiMate 3000 LC System (Thermo Scientific, Waltham, Massachusetts, EUA) coupled to a Q Exactive Orbitrap Mass Spectrometer (Thermo Scientific, Waltham, Massachusetts, EUA) operating in polarity switch mode. Chromatographic separation was achieved using a ZIC®-pHILIC 150 × 2.1 mm column (Merck MilliporeSigma, Burlington, Massachusetts, EUA) using a gradient starting from 20% buffer A (20 mM ammonium carbonate 0.1% ammonium hydroxide solution 25%), and 80% B (acetonitrile) to 80% buffer A, 20% buffer B at 18 min and reconditioning the column to the initial condition until 27.5 min. Mass spectrometry data were processed using Skyline [ 45 ] on a targeted fashion by matching accurate mass and retention time using a in house library acquired from authentic standards. Statistical analysis was performed using metaboanalyst 5.0 [ 46 ].

Diagrams were created using Biorender under a Premium Plan (K.B.M).

Quantification and statistical analysis

Statistical analyses were performed using GraphPad Prism software (v8.3 GraphPad Software, La Jolla, CA, USA) performing the tests as indicated in the figure legends or main text. Significance levels were calculated according: p < 0.05 (*), p < 0.01(**) and p < 0.001 (***). P values are reported on graphs where comparisons are statistically significant. On graphs with multiple comparisons, for clarity, non-significant changes are not shown.

Contact for reagent and resource sharing

Requests for further information, reagents and resources should be directed and will be fulfilled by the Lead Contact, Kevin B Myant: ([email protected]).

Data availability

All relevant data is available from the authors upon request.

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Acknowledgements

This work was funded by a Wellcome Trust clinical research fellowship through the Edinburgh Clinical Academic Track (ECAT) programme, 214369/Z/18/Z (AMDC), as well as Cancer Research UK (CRUK) under Career Development Fellowship, A19166 (KBM) and Small Molecule Drug Discovery Project Award, A25808 (KBM), the European Research Council under Starting Grant, COLGENES – 715782 (KBM), Marie Skłodowska Curie Actions European Fellowship (GDCOLCA 800112; to TS); Naito Foundation Grant for Research Abroad (to TS) and Career Establishment Award RCCCEA-Nov21\100003; to SB. Coffelt. Metabolomics was supported by the Wellcome Trust (Multiuser Equipment Grant 208402/Z/17/Z). We thank the University of Edinburgh’s Institute of Genetics and Cancer (IGC) technical staff for providing support for some of the experiments and we thank the animal technicians at the Biomedical Research Facility (BRF) facility for animal husbandry support. We thank Prof Owen Sansom for providing us with the villin-cre ERT2 and Apc fl mouse lines. Finally, we thank Lothian NRS BioResource for access to human pathological slides. SD acknowledges the support of NHS Research Scotland via NHS Lothian.

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Antonia M. D. Churchhouse, Caroline V. Billard, Sebastian Ö. G. Pohl, Nora J. Doleschall, Kevin Donnelly, Mark J. Arends, Jair Marques Junior, Alex Von Kriegsheim & Kevin B. Myant

Cancer Research UK Scotland Institute, Garscube Estate, Glasgow, UK

Toshiyasu Suzuki, Colin Nixon & Seth B. Coffelt

School of Cancer Sciences, University of Glasgow, Glasgow, UK

Toshiyasu Suzuki & Seth B. Coffelt

Edinburgh IBD Unit, Western General Hospital, Edinburgh, UK

Shahida Din

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Contributions

AMDC helped with study design and carried out the majority of experiments. CVB provided technical support for animal work, carried out some animal experiments and carried out histological analysis. SOGP carried out bioinformatics analysis of RNAseq data and investigated IDO1 induction by IFNγ. KD carried out bioinformatics analysis of RNAseq data. TS helped with study design for immune analysis and carry out flow cytometry analysis of immune cell populations and discussed the results and contributed to the final manuscript. NJD carried out the coIF experiments. MJA, SD, and KK provided human tissue samples and assistance with histological analysis alongside Colin Nixon. JMJ and AVK carried out the metabolomic analysis. SBC provided assistance with immune cell characterisation and analysis and interpretation of results. KBM designed the project and helped support the experiments. AMDC, SBC, and KBM wrote the manuscript.

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Correspondence to Kevin B. Myant .

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All methods were performed in accordance with relevant guidelines and regulations. Approval for animal experiments has been obtained from the animal welfare and ethics board of the University of Edinburgh and all animal experiments were performed in accordance with a UK Home Office license (Project License 70/8885). Informed consent was obtained from all human subjects providing samples for analysis (Sample Requests SR1165 and SR1165- AM01 301120; Ethical Approvals 15/ES/0094 and 20/ES/0061).

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Churchhouse, A.M.D., Billard, C.V., Suzuki, T. et al. Loss of DOCK2 potentiates Inflammatory Bowel Disease–associated colorectal cancer via immune dysfunction and IFNγ induction of IDO1 expression. Oncogene (2024). https://doi.org/10.1038/s41388-024-03135-9

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Received : 08 August 2023

Revised : 05 August 2024

Accepted : 13 August 2024

Published : 07 September 2024

DOI : https://doi.org/10.1038/s41388-024-03135-9

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