statistics assignment examples

[email protected]

• SCHOLARSHIP

Statistics Assignment Examples

• February 2, 2024

Introduction: Embarking on the intricate journey of statistics assignments can be both challenging and enlightening. To navigate this terrain successfully, gaining a deep understanding of statistical concepts is crucial. In this comprehensive exploration, we’ll delve into the world of statistics assignment examples, unraveling the complexities through real-world scenarios. Whether you’re a student seeking clarity for your assignments or an enthusiast eager to grasp the practical applications of statistics, join us on this illuminating journey with Statistics Homework Tutors. Together, we’ll dissect key statistical concepts using practical examples, providing you with the tools to master this field and transform challenges into triumphs.

The Essence of Descriptive Statistics: Shedding Light on Performance

Example 1: exam scores.

Imagine you’re a student in a class of 30, eagerly awaiting your math exam scores. Descriptive statistics come to the rescue, turning those numbers into meaningful insights:

• Average (Mean): Calculating the sum of all scores and dividing by the number of students provides a snapshot of the overall performance.
• Middle Value (Median): Arranging scores in ascending order and identifying the middle score offers a representation of central tendency.
• Most Frequent Score (Mode): Spotting the score that appears most frequently gives a sense of the prevailing performance level.

This statistical approach transforms a seemingly random set of scores into a clear understanding of the class’s academic standing.

Harnessing the Power of Inferential Statistics: Predicting Outcomes

Example 2: opinion polls.

In the world of politics, predicting election outcomes is a task tailor-made for inferential statistics. Imagine conducting an opinion poll with 1,000 participants to gauge voter preferences:

• Hypothesis Testing: Evaluating whether a particular candidate’s popularity significantly differs from others provides insights into voter sentiment.
• Confidence Intervals: Estimating the range within which the true proportion of voters supporting a candidate lies adds a layer of certainty to predictions.

Inferential statistics empowers us to draw meaningful conclusions about a larger population based on a representative sample.

Business Optimization through Regression Analysis

Example 3: sales and advertising.

Enter the dynamic world of business, where regression analysis plays a pivotal role in optimizing advertising strategies. Consider a scenario where a company invests in advertising campaigns over several months:

• Modeling Relationships: Establishing a mathematical relationship between advertising expenses and sales unveils the dynamics at play.
• Predicting Outcomes: Anticipating the expected increase in sales for a given increase in advertising spending guides decision-making.

Regression analysis transforms data into actionable insights, aiding businesses in making informed choices to maximize their impact.

Quantifying Uncertainty with Probability Distributions

Example 4: coin toss.

Dive into the world of probability distributions with a simple coin toss. The unpredictability of this event is quantified through:

• Likelihood of Outcomes: A fair coin offers a 50% chance of landing on heads or tails, illustrating the fundamental concept of probability.
• Expected Value: The average outcome over many tosses converges to 0.5, showcasing the stability of probability predictions.

Understanding probability distributions is fundamental to making sense of uncertainties and predicting outcomes in various scenarios.

Real-World Impact: Statistics in Healthcare

Example 5: medical diagnosis.

In the critical realm of healthcare, statistics takes center stage in medical diagnosis. Consider a diagnostic test for a rare disease:

• Sensitivity and Specificity: Evaluating the accuracy of the test in identifying true positives and true negatives helps in assessing its reliability.
• False Positive Rate: Examining the likelihood of a healthy person receiving an incorrect diagnosis adds a layer of caution.

Statistics in healthcare guides medical professionals in interpreting test results, making accurate diagnoses, and ultimately improving patient outcomes.

The Role of Statistics Homework Tutors: Tailored Support for Success

With these examples in mind, it’s crucial to acknowledge the role of Statistics Homework Tutors in enhancing your journey through the statistical landscape. As a student-centric platform, Statistics Homework Tutors provides:

Tailored Support for Students:

Recognizing the unique challenges faced by students, the platform’s approach is tailored to simplify complexities, ensuring a profound understanding of statistical principles.

Expert Tutors in Statistics:

Access to a team of expert tutors with specialized knowledge in statistics guarantees students receive guidance from professionals who excel not only in subject matter expertise but also in effective communication.

Application-Oriented Learning:

Moving beyond theoretical concepts, Statistics Homework Tutors emphasizes practical examples and applications, making statistics more relevant to real-world scenarios.

Timely Assistance:

Acknowledging the time constraints students often encounter, Statistics Homework Tutors provides timely assistance, ensuring that deadlines are met without compromising the quality of students’ work.

The Path to Mastery: Statistics Homework Tutors’ Approach

1. illuminating concepts:.

Statistics Homework Tutors illuminates complex statistical concepts through real-world examples, making them more accessible and applicable.

2. Enhancing Understanding:

By providing tailored support, the platform enhances students’ understanding of statistics, ensuring they grasp both the theory and practical applications.

3. Real-Time Assistance:

Offering real-time assistance, Statistics Homework Tutors ensures that students can navigate challenges and overcome hurdles with expert guidance.

4. Promoting Application:

Encouraging application-oriented learning, the platform equips students with the skills to apply statistical methods to various scenarios.

5. Secure and Confidential:

Prioritizing the security and confidentiality of student information, Statistics Homework Tutors ensures a safe and trustworthy learning environment.

Conclusion: Your Journey to Statistical Mastery

As we conclude this comprehensive exploration of statistics and its real-world applications, it’s evident that mastering this field is not just about crunching numbers but understanding the stories they tell. Statistics Homework Tutors serves as a guiding light on your journey to statistical mastery, offering tailored support, expert guidance, and real-world examples that transform challenges into opportunities for growth.

Recent Blogs

The Impact of Social Media on Mental Health: Stats on Anxiety and Depression

The Growing Influence of Podcasts: Stats on Listenership and Marketing Potential

Artificial Intelligence in Customer Service: Stats on Adoption and Impact

• Statistics Assignment Help 124
• Statistics Homework Help 111
• Statistics Project Help 131

Share this post

Related posts.

Social media has become an undeniable force in our lives. While it offers connection, entertainment, and information, a growing body of research suggests a potential

Podcasts have exploded in popularity in recent years, evolving from a niche hobbyist medium to a mainstream form of entertainment and information dissemination. With a

Artificial intelligence (AI) is rapidly transforming the customer service landscape. From chatbots handling basic inquiries to AI-powered analytics optimizing support processes, AI is making significant

Keep in touch with the trends

7 street, WC2H 9JQ, Dallas Texas, United States of America

• Our Experts
• Terms & Conditions
• Privacy Policy
• Refund Policy
• Probability Help
• Chi-Squared
• Econometrics

75+ Realistic Statistics Project Ideas For Students To Score A+

Statistics is one of the major subjects for every student, even in high school or college. These days almost every student is searching for the best, and more practical statistics project ideas. Even if you are a humanities, science or commerce student, you should have a good command of it. 

Statistics has many sub-topics such as normal curves, regression, correlation, statistical inference, and many more. But keep in mind that the difficulty level of statistics varies from your study level. It means that statistics concepts can be more difficult for college students than for school students. It implies that statistical project topics would be different for college students and school students. On the other hand, if you are looking for statistics assignment help , then you can get the best assignment help from us.

But before we unveil these good statistics project ideas. Let’s understand what a statistical project is.

What is a Statistical Project?

Table of Contents

A statistical project is the best process of answering the research questions using statistical terminologies and techniques. It also helps us to present the work written in the given report. In statistical projects, the research could be on scientific or generic fields such as advertising, nutrition, and lots more. Therefore the difficulty level of statistical projects varies with research topics. And the statistics concepts also differ from one case to another. You can also visit statanalytica blogs to get assistance for statistical projects assignment idea.

What are Statistics Topics?

There are tons of topics in statistics. The most common statistics topics are normal curves, binomials, regression, correlation, permutation and combinations, statistical inference, and more. And all the statics topics are applicable in our daily life. Whether it is the tech or entertainment industry, everyone uses statistics topics. 

Tips for finding easy statistics project ideas

Finding the best and easiest statistics project is not an easy task. But here are some of the best tips that will help you to find easy statistics project ideas:-

• Deeply analyze the data presented by the research 
• Do you have an affirmative statement of the problems that have initiated the research? 
• Study summary based on your research
• Have a deep discussion of the students’ design to clarify the problem. 

All these steps will help you to find the best statistics project ideas. The next step is to write down the essential component of the statistics paper, i.e.:-

• Data analysis (by understanding the importance of data analytics projects )
• Statement of the problem
• Summary and conclusion
• Research design

Although if you follow these steps precisely, you will surely find the best project on statistics. But we are here to make it easy for you; let’s have a look at 

Statistics Project Ideas for High School

Let’s find out the best statistics project ideas for high school that will help you to score good grades and showcase your skills:-

• Categorize the researched raw data into qualitative or quantitative
• Evaluate the published reports and graphs based on the analyzed data and conclude.
• Use dice to evaluate the bias and effect of completing data.
• Discuss the factors that can affect the result of the given survey data.
• Increasing use of plastic.
• Are e-books better than conventional books?
• Do extra-curricular activities help transform personalities?
• Should stereotypical social issues be highlighted or not?
• Should mobile phones be allowed in high schools or not?
• The Significance of Medication in Class Performance.
• Does the effect of a teacher who is a fresher at university influence the student’s performance?
• Influence of Distinct Subjects on Students’ Performance.
• Caffeine consumption among students as well as its effect on performance.
• Are online classes helpful?
• Influence of better students in class.
• The significance of the front seats in the class on success rates. Does an online brochure creator reduce marketing costs?

Additional statistics project examples:

The use of mobile phones in the classroom is always a debatable topic. Therefore, it is always a good statistics project idea to write statistics about how many students and teachers are in favor of using mobile phones in the classroom.

Small Business Statistics Project Topics

• The impact of the pandemic on small business survival rates.
• Analysis of the most profitable industries for small businesses.
• Small business failure rates by region and industry.
• The relationship between access to funding and small business success rates.
• The impact of social media marketing (SMM) on small business growth.
• The role of e-commerce in small business growth.
• The impact of government regulations on small business success rates.
• The gender gap in small business ownership and success rates.
• The impact of employee retention on small business growth and success rates.
• The relationship between small business growth and community development.
• The impact of the gig economy on small business growth.
• Analysis of the most common reasons for small business failure.
• The role of technology in small business growth and success rates.
• The impact of competition on small business survival rates.
• The relationship between small business ownership and educational attainment.

Statistics Project Ideas on Socio-Economics

• Income versus explanation analysis in society.
• Peak traffic times in your city.
• The significance of agricultural loans for farmers.
• Food habits in low-income families.
• Malpractices of low-income groups.
• Analysis of road accidents in the suburb and the town area.
• The effect of smoking on medical costs.
• Regression analysis on national income.
• Income vs Consumption Explanation Study in Society.
• A Study of the Worldwide Economic Growth
• The Influence of the Pandemic on Health in the UK
• Influence of Advertisement on Health Costs
• The effect of poverty on crime rates.
• Do federal elections affect stock prices?

Statistics Project Ideas for University Students (2023)

• Analyzing the impact of COVID-19 on a particular industry or economic sector.
• Examining the relationship between income and health outcomes in a particular population or geographic area.
• Investigate the factors influencing student success in a particular course or academic program.
• Analyzing the effectiveness of a specific marketing campaign or promotional strategy.
• Evaluating the relationship between social media usage and mental health outcomes.
• Examining the impact of climate change on a particular ecosystem or species.
• Investigating the factors influencing voter turnout in a particular election or geographic area.
• Analyzing the relationship between exercise and mental health outcomes.
• Evaluating the effectiveness of a particular intervention or program in addressing a specific social issue, such as poverty or homelessness.
• Examining the relationship between crime rates and economic conditions in a particular area.

Statistics Survey Project Ideas

Let’s find out some of the best statistics survey project ideas. Here we go:-

• Have a deep statistics analysis on the pollution level across various cities worldwide.
• Find out the most selling smartphones globally and used by college students.
• Do the behavioral survey of Omicron variant patients across the world. 
• Conduct a survey about the global warming world.

Sometimes conducting a survey is itself a headache for you. That is why it is better to get easy statistics to project ideas. A survey report on E-books vs Textbooks is a good idea for students to conduct a survey and write down all useful insights collected from the survey report.

Statistics Project Ideas Hypothesis Testing

Statistics project ideas for hypothesis testing are not for everyone. But have a look at some of the best statistic project examples for hypothesis testing:-

• Peppermint essential oil affects the pangs of anxiety
• Immunity during winter for students who take more vitamin C than those who don’t.
• The productivity level of young boys as compared with the young girls.
• Obesity level of children whose parents are obese. 

Hypothesis testing plays an important role in concluding the most estimated result of the experiment. That is why we always suggest students conduct the hypothesis test for the present situation. Like you consider the students’ choice regarding the subjects. And write the statistical factors, like whether students select their subject based on the industry’s stability or as per their liking.

AP Statistics Project Ideas

Let’s have a look at some of the AP statistics project ideas. If statistics are your primary subject, these projects will impact your grades. 

• Find out the impact of school jobs and activities on the student’s overall grades.
• Who influences the children more on religious views, either the month or the father?
• Are age and sleeping related to each other, i.e., adult people tend to sleep less than kids and old-age citizens?
• Does plastic surgery change the perspective towards you the people?

To show the study of AP statistics project ideas, you need to offer arguments based on the evidence, perform research, and analyze the issues. You can write a statistics project based on alcohol advertisements and their effect on younger people of these ads. 

Statistics Final Project Ideas

A massive number of students look for statistics and final project ideas. Have a look at some of the best final projects in statistics:-

• Do high heel sandals harm the body posture of the lady?
• Does the patient’s intelligence also affect the brilliance of the child?
• Is there any relation to eating hotdogs while watching a baseball match in the stadium?
• Does an opinion poll change the initially perceived election results?

If you are a final-year student looking for exciting project ideas, write a statistical report on the regression analysis. The analysis can be done on the national income, and you can put all the ins-outs on this topic with a detailed report.

Two variable statistics project Ideas

Have a look at the two-variable statistics project where one variable affects the other one:-

• Are electric cars a good choice to have control over global warming?
• Investing in FDIs can help the country to grow its GDP.
• Is lockdown the best solution to stop the spread of Coronavirus?
• Investing in cryptocurrency can have a significant impact on your future.

Statistics Project Ideas for College Students

There are tons of college statistics project examples. But we will share the best ideas for statistics projects for the college. As we have already discussed, college statistics project ideas are pretty complex compared with school-level projects. Let’s have a look at the best statistics project ideas for college:- 

• Excessive use of the internet reduces the creativity and innovation skills of the students.
• The use of social media has bypassed studying in the students’ free time.
• Can college students develop drug habits if given a chance?
• Does a college freshman’s experience with their roommate affect their overall experience at the institution?
• A comparative study on the pricing of different clothing stores in your town.
• College students’ Web browsing habits.
• Comparison between male and female students in college.
• Statistical analysis of the highway accidents in your local neighborhood.
• Students in college choose common subjects.
• Choosing aspects of a subject in college.
• Course price differentiation in colleges.
• There is less interest in the students in humanities subjects as compared with science and technology.
• Relationship between birth order as well as academic success.
• Is being headstrong difficult, or does it make things easier?
• Popular movie genre among students in college.
• What kinds of music do college students like the most?
• Difference between the male and female population in a city based on their age. 
• The Significance of Analytics in Studying Statistics
• Influence of backbenchers on their performance in class.

Fun Statistics Project Ideas

Have a look at some of the statistics projects examples:-

• Most of the volleyball players are tall compared with a few short ones.
• Men tend to have more interest in cricket as compared with females.
• Shorter and chubby girls are more friendly than tall and skinny girls.
• Aggression between students is based on the environment where they grew up.
• Students involved in co-curricular activities tend to have lower grades than those who don’t.
• Highly pressured employees consume more alcohol than those who do repetitive tasks jobs.

The Point With Statistics Projects Ideas

To write an impressive statistical project, you need to follow some points. Let’s have a look at these points:- 

• Always work with organized information. If you get unorganized data, try to organize it first and then start working.
• Start with an outline, and it will help you to organize the final data of your statistics project. For this, you can also look at previous statistics project examples.
• Always write for the beginner’s audience. Don’t expect that your audience already knows everything. For this, be brief, simple, and to the point.
• Don’t miss the citation because it always helps showcase your projects’ authenticity. And keep the citation in the given format.  
• The outcome of your statistical test should refer to the hypothesis being tested.
• If you have spent lots of time researching your project, you can take the help of statistics project writing services. For this, you can approach statistics homework help experts, and they will offer you the best statistics projects on your researched idea. 
• Don’t get anxious while doing your statistics projects. Because most of the time, the professors give the research questions to the students. And the students need to collect, analyze, and interpret the information to provide the most suitable answer or conclusion to the question using statistical methods and techniques. 

There are plenty of tons or even thousands of statistics project ideas to work on. But in this blog, I have mentioned some of the best and more realistic statistics project ideas. If you work on any of these ideas, you will not just get good grades but will also enjoy your project while working on it. As the quote said, “Do what you love, love what you do.”

Also, follow the steps mentioned at the end of the blog to finish up with the best-in-class statistics project. We have covered these ideas for almost every student. But still, if you are not able to find the best project for you, you should get in touch with our experts. Our team of experts will instantly get in touch with you and help you find the most suitable statistics project ideas for you. 

Q1. What is meant by statistical project?

Statistics projects are a paper used to present the comprehension analysis of gathering statistical data. It contains the statistical data for the collected statistical data. In other words, it brings the significant results of a specific research question. 

Q2. What are some practical uses for statistics in everyday life?

Many people use statistics to make decisions in budgeting and financial planning. On the other hand, most banks use statistics to lower the risk of lending operations, predict the impact of economic crises, and analyze activity in the financial market.

Related Posts

How to Find the Best Online Statistics Homework Help

Why SPSS Homework Help Is An Important aspect for Students?

Introduction to Statistics

(15 reviews)

David Lane, Rice University

Copyright Year: 2003

Publisher: David Lane

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Terri Torres, professor, Oregon Institute of Technology on 8/17/23

This author covers all the topics that would be covered in an introductory statistics course plus some. I could imagine using it for two courses at my university, which is on the quarter system. I would rather have the problem of too many topics... read more

Comprehensiveness rating: 5 see less

This author covers all the topics that would be covered in an introductory statistics course plus some. I could imagine using it for two courses at my university, which is on the quarter system. I would rather have the problem of too many topics rather than too few.

Content Accuracy rating: 5

Yes, Lane is both thorough and accurate.

Relevance/Longevity rating: 5

What is covered is what is usually covered in an introductory statistics book. The only topic I may, given sufficient time, cover is bootstrapping.

Clarity rating: 5

The book is clear and well-written. For the trickier topics, simulations are included to help with understanding.

Consistency rating: 5

All is organized in a way that is consistent with the previous topic.

Modularity rating: 5

The text is organized in a way that easily enables navigation.

Organization/Structure/Flow rating: 5

The text is organized like most statistics texts.

Interface rating: 5

Easy navigation.

Grammatical Errors rating: 5

I didn't see any grammatical errors.

Cultural Relevance rating: 5

Nothing is included that is culturally insensitive.

The videos that accompany this text are short and easy to watch and understand. Videos should be short enough to teach, but not so long that they are tiresome. This text includes almost everything: videos, simulations, case studies---all nicely organized in one spot. In addition, Lane has promised to send an instructor's manual and slide deck.

Reviewed by Professor Sandberg, Professor, Framingham State University on 6/29/21

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful. read more

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful.

I did not find any errors.

Some of the examples are dated. And the frequent use of male/female examples need updating in terms of current gender splits.

I found it was easy to read and understand and I expect that students would also find the writing clear and the explanations accessible.

Even with different authors of chapter, the writing is consistent.

The text is well organized into sections making it easy to assign individual topics and sections.

The topics are presented in the usual order. Regression comes later in the text but there is a difference of opinions about whether to present it early with descriptive statistics for bivariate data or later with inferential statistics.

I had no problem navigating the text online.

The writing is grammatical correct.

I saw no issues that would be offensive.

I did like this text. It seems like it would be a good choice for most introductory statistics courses. I liked that the Monty Hall problem was included in the probability section. The author offers to provide an instructor's manual, PowerPoint slides and additional questions. These additional resources are very helpful and not always available with online OER texts.

Reviewed by Emilio Vazquez, Associate Professor, Trine University on 4/23/21

This appears to be an excellent textbook for an Introductory Course in Statistics. It covers subjects in enough depth to fulfill the needs of a beginner in Statistics work yet is not so complex as to be overwhelming. read more

This appears to be an excellent textbook for an Introductory Course in Statistics. It covers subjects in enough depth to fulfill the needs of a beginner in Statistics work yet is not so complex as to be overwhelming.

I found no errors in their discussions. Did not work out all of the questions and answers but my sampling did not reveal any errors.

Some of the examples may need updating depending on the times but the examples are still relevant at this time.

This is a Statistics text so a little dry. I found that the derivation of some of the formulas was not explained. However the background is there to allow the instructor to derive these in class if desired.

The text is consistent throughout using the same verbiage in various sections.

The text dose lend itself to reasonable reading assignments. For example the chapter (Chapter 3) on Summarizing Distributions covers Central Tendency and its associated components in an easy 20 pages with Measures of Variability making up most of the rest of the chapter and covering approximately another 20 pages. Exercises are available at the end of each chapter making it easy for the instructor to assign reading and exercises to be discussed in class.

The textbook flows easily from Descriptive to Inferential Statistics with chapters on Sampling and Estimation preceding chapters on hypothesis testing

I had no problems with navigation

All textbooks have a few errors but certainly nothing glaring or making text difficult

I saw no issues and I am part of a cultural minority in the US

Overall I found this to be a excellent in-depth overview of Statistical Theory, Concepts and Analysis. The length of the textbook appears to be more than adequate for a one-semester course in Introduction to Statistics. As I no longer teach a full statistics course but simply a few lectures as part of our Research Curriculum, I am recommending this book to my students as a good reference. Especially as it is available on-line and in Open Access.

Reviewed by Audrey Hickert, Assistant Professor, Southern Illinois University Carbondale on 3/29/21

All of the major topics of an introductory level statistics course for social science are covered. Background areas include levels of measurement and research design basics. Descriptive statistics include all major measures of central tendency and... read more

All of the major topics of an introductory level statistics course for social science are covered. Background areas include levels of measurement and research design basics. Descriptive statistics include all major measures of central tendency and dispersion/variation. Building blocks for inferential statistics include sampling distributions, the standard normal curve (z scores), and hypothesis testing sections. Inferential statistics include how to calculate confidence intervals, as well as conduct tests of one-sample tests of the population mean (Z- and t-tests), two-sample tests of the difference in population means (Z- and t-tests), chi square test of independence, correlation, and regression. Doesn’t include full probability distribution tables (e.g., t or Z), but those can be easily found online in many places.

I did not find any errors or issues of inaccuracy. When a particular method or practice is debated in the field, the authors acknowledge it (and provide citations in some circumstances).

Relevance/Longevity rating: 4

Basic statistics are standard, so the core information will remain relevant in perpetuity. Some of the examples are dated (e.g., salaries from 1999), but not problematic.

Clarity rating: 4

All of the key terms, formulas, and logic for statistical tests are clearly explained. The book sometimes uses different notation than other entry-level books. For example, the variance formula uses "M" for mean, rather than x-bar.

The explanations are consistent and build from and relate to corresponding sections that are listed in each unit.

Modularity is a strength of this text in both the PDF and interactive online format. Students can easily navigate to the necessary sections and each starts with a “Prerequisites” list of other sections in the book for those who need the additional background material. Instructors could easily compile concise sub-sections of the book for readings.

The presentation of topics differs somewhat from the standard introductory social science statistics textbooks I have used before. However, the modularity allows the instructor and student to work through the discrete sections in the desired order.

Interface rating: 4

For the most part the display of all images/charts is good and navigation is straightforward. One concern is that the organization of the Table of Contents does not exactly match the organizational outline at the start of each chapter in the PDF version. For example, sometimes there are more detailed sub-headings at the start of chapter and occasionally slightly different section headings/titles. There are also inconsistencies in section listings at start of chapters vs. start of sub-sections.

The text is easy to read and free from any obvious grammatical errors.

Although some of the examples are outdated, I did not review any that were offensive. One example of an outdated reference is using descriptive data on “Men per 100 Women” in U.S. cities as “useful if we are looking for an opposite-sex partner”.

This is a good introduction level statistics text book if you have a course with students who may be intimated by longer texts with more detailed information. Just the core basics are provided here and it is easy to select the sections you need. It is a good text if you plan to supplement with an array of your own materials (lectures, practice, etc.) that are specifically tailored to your discipline (e.g., criminal justice and criminology). Be advised that some formulas use different notation than other standard texts, so you will need to point that out to students if they differ from your lectures or assessment materials.

Reviewed by Shahar Boneh, Professor, Metropolitan State University of Denver on 3/26/21, updated 4/22/21

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course. read more

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course.

The text seems to be statistically accurate.

It is a little too extensive, which requires instructors to cover it selectively, and has a potential to confuse the students.

It is written clearly.

Consistency rating: 4

The terminology is fairly consistent. There is room for some improvement.

By the nature of the subject, the topics have to be presented in a sequential and coherent order. However, the book breaks things down quite effectively.

Organization/Structure/Flow rating: 3

Some of the topics are interleaved and not presented in the order I would like to cover them.

Good interface.

The grammar is ok.

The book seems to be culturally neutral, and not offensive in any way.

I really liked the simulations that go with the book. Parts of the book are a little too advanced for students who are learning statistics for the first time.

Reviewed by Julie Gray, Adjunct Assistant Professor, University of Texas at Arlington on 2/26/21

The textbook is for beginner-level students. The concept development is appropriate--there is always room to grow to high higher level, but for an introduction, the basics are what is needed. This is a well-thought-through OER textbook project by... read more

The textbook is for beginner-level students. The concept development is appropriate--there is always room to grow to high higher level, but for an introduction, the basics are what is needed. This is a well-thought-through OER textbook project by Dr. Lane and colleagues. It is obvious that several iterations have only made it better.

I found all the material accurate.

Essentially, statistical concepts at the introductory level are accepted as universal. This suggests that the relevance of this textbook will continue for a long time.

The book is well written for introducing beginners to statistical concepts. The figures, tables, and animated examples reinforce the clarity of the written text.

Yes, the information is consistent; when it is introduced in early chapters it ties in well in later chapters that build on and add more understanding for the topic.

Modularity rating: 4

The book is well-written with attention to modularity where possible. Due to the nature of statistics, that is not always possible. The content is presented in the order that I usually teach these concepts.

The organization of the book is good, I particularly like the sample lecture slide presentations and the problem set with solutions for use in quizzes and exams. These are available by writing to the author. It is wonderful to have access to these helpful resources for instructors to use in preparation.

I did not find any interface issues.

The book is well written. In my reading I did not notice grammatical errors.

For this subject and in the examples given, I did not notice any cultural issues.

For the field of social work where qualitative data is as common as quantitative, the importance of giving students the rationale or the motivation to learn the quantitative side is understated. To use this text as an introductory statistics OER textbook in a social work curriculum, the instructor will want to bring in field-relevant examples to engage and motivate students. The field needs data-driven decision making and evidence-based practices to become more ubiquitous than not. Preparing future social workers by teaching introductory statistics is essential to meet that goal.

Reviewed by Mamata Marme, Assistant Professor, Augustana College on 6/25/19

This textbook offers a fairly comprehensive summary of what should be discussed in an introductory course in Statistics. The statistical literacy exercises are particularly interesting. It would be helpful to have the statistical tables... read more

Comprehensiveness rating: 4 see less

This textbook offers a fairly comprehensive summary of what should be discussed in an introductory course in Statistics. The statistical literacy exercises are particularly interesting. It would be helpful to have the statistical tables attached in the same package, even though they are available online.

The terminology and notation used in the textbook is pretty standard. The content is accurate.

The statistical literacy example are up to date but will need to be updated fairly regularly to keep the textbook fresh. The applications within the chapter are accessible and can be used fairly easily over a couple of editions.

The textbook does not necessarily explain the derivation of some of the formulae and this will need to be augmented by the instructor in class discussion. What is beneficial is that there are multiple ways that a topic is discussed using graphs, calculations and explanations of the results. Statistics textbooks have to cover a wide variety of topics with a fair amount of depth. To do this concisely is difficult. There is a fine line between being concise and clear, which this textbook does well, and being somewhat dry. It may be up to the instructor to bring case studies into the readings we are going through the topics rather than wait until the end of the chapter.

The textbook uses standard notation and terminology. The heading section of each chapter is closely tied to topics that are covered. The end of chapter problems and the statistical literacy applications are closely tied to the material covered.

The authors have done a good job treating each chapter as if they stand alone. The lack of connection to a past reference may create a sense of disconnect between the topics discussed

The text's "modularity" does make the flow of the material a little disconnected. If would be better if there was accountability of what a student should already have learnt in a different section. The earlier material is easy to find but not consistently referred to in the text.

I had no problem with the interface. The online version is more visually interesting than the pdf version.

I did not see any grammatical errors.

Cultural Relevance rating: 4

I am not sure how to evaluate this. The examples are mostly based on the American experience and the data alluded to mostly domestic. However, I am not sure if that creates a problem in understanding the methodology.

Overall, this textbook will cover most of the topics in a survey of statistics course.

Reviewed by Alexandra Verkhovtseva, Professor, Anoka-Ramsey Community College on 6/3/19

This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range... read more

This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range of intro stats topics (and some more), plus the case studies and the glossary.

The content is pretty accurate, I did not find any biases or errors.

The book contains fairly recent data presented in the form of exercises, examples and applications. The topics are up-to-date, and appropriate technology is used for examples, applications, and case studies.

The language is simple and clear, which is a good thing, since students are usually scared of this class, and instructors are looking for something to put them at ease. I would, however, try to make it a little more interesting, exciting, or may be even funny.

Consistency is good, the book has a great structure. I like how each chapter has prerequisites and learner outcomes, this gives students a good idea of what to expect. Material in this book is covered in good detail.

The text can be easily divided into sub-sections, some of which can be omitted if needed. The chapter on regression is covered towards the end (chapter 14), but part of it can be covered sooner in the course.

The book contains well organized chapters that makes reading through easy and understandable. The order of chapters and sections is clear and logical.

The online version has many functions and is easy to navigate. This book also comes with a PDF version. There is no distortion of images or charts. The text is clean and clear, the examples provided contain appropriate format of data presentation.

No grammatical errors found.

The text uses simple and clear language, which is helpful for non-native speakers. I would include more culturally-relevant examples and case studies. Overall, good text.

In all, this book is a good learning experience. It contains tools and techniques that free and easy to use and also easy to modify for both, students and instructors. I very much appreciate this opportunity to use this textbook at no cost for our students.

Reviewed by Dabrina Dutcher, Assistant Professor, Bucknell University on 3/4/19

This is a reasonably thorough first-semester statistics book for most classes. It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for... read more

This is a reasonably thorough first-semester statistics book for most classes. It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for engineers or business applications. That is OK, they have separate texts for that! The only sections that feel somewhat light in terms of content are the confidence intervals and ANOVA sections. Given that these topics are often sort of crammed in at the end of many introductory classes, that might not be problematic for many instructors. It should also be pointed out that while there are a couple of chapters on probability, this book spends presents most formulas as "black boxes" rather than worry about the derivation or origin of the formulas. The probability sections do not include any significant combinatorics work, which is sometimes included at this level.

I did not find any errors in the formulas presented but I did not work many end-of-chapter problems to gauge the accuracy of their answers.

There isn't much changing in the introductory stats world, so I have no concerns about the book becoming outdated rapidly. The examples and problems still feel relevant and reasonably modern. My only concern is that the statistical tool most often referenced in the book are TI-83/84 type calculators. As students increasingly buy TI-89s or Inspires, these sections of the book may lose relevance faster than other parts.

Solid. The book gives a list of key terms and their definitions at the end of each chapter which is a nice feature. It also has a formula review at the end of each chapter. I can imagine that these are heavily used by students when studying! Formulas are easy to find and read and are well defined. There are a few areas that I might have found frustrating as a student. For example, the explanation for the difference in formulas for a population vs sample standard deviation is quite weak. Again, this is a book that focuses on sort of a "black-box" approach but you may have to supplement such sections for some students.

I did not detect any problems with inconsistent symbol use or switches in terminology.

Modularity rating: 3

This low rating should not be taken as an indicator of an issue with this book but would be true of virtually any statistics book. Different books still use different variable symbols even for basic calculated statistics. So trying to use a chapter of this book without some sort of symbol/variable cheat-sheet would likely be frustrating to the students.

However, I think it would be possible to skip some chapters or use the chapters in a different order without any loss of functionality.

This book uses a very standard order for the material. The chapter on regressions comes later than it does in some texts but it doesn't really matter since that chapter never seems to fit smoothly anywhere.

There are numerous end of chapter problems, some with answers, available in this book. I'm vacillating on whether these problems would be more useful if they were distributed after each relevant section or are better clumped at the end of the whole chapter. That might be a matter of individual preference.

I did not detect any problems.

I found no errors. However, there were several sections where the punctuation seemed non-ideal. This did not affect the over-all useability of the book though

I'm not sure how well this book would work internationally as many of the examples contain domestic (American) references. However, I did not see anything offensive or biased in the book.

Reviewed by Ilgin Sager, Assistant Professor, University of Missouri - St. Louis on 1/14/19

As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive text on the subject. A teacher can use this book as the sole text of an introductory statistics.... read more

As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive text on the subject. A teacher can use this book as the sole text of an introductory statistics. The prose format of definitions and theorems make theoretical concepts accessible to non-math major students. The textbook covers all chapters required in this level course.

It is accurate; the subject matter in the examples to be up to date, is timeless and wouldn't need to be revised in future editions; there is no error except a few typographical errors. There are no logic errors or incorrect explanations.

This text will remain up to date for a long time since it has timeless examples and exercises, it wouldn't be outdated. The information is presented clearly with a simple way and the exercises are beneficial to follow the information.

The material is presented in a clear, concise manner. The text is easy readable for the first time statistics student.

The structure of the text is very consistent. Topics are presented with examples, followed by exercises. Problem sets are appropriate for the level of learner.

When the earlier matters need to be referenced, it is easy to find; no trouble reading the book and finding results, it has a consistent scheme. This book is set very well in sections.

The text presents the information in a logical order.

The learner can easily follow up the material; there is no interface problem.

There is no logic errors and incorrect explanations, a few typographical errors is just to be ignored.

Not applicable for this textbook.

Reviewed by Suhwon Lee, Associate Teaching Professor, University of Missouri on 6/19/18

This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises,... read more

This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises, review questions, and practice tests. It provides references and case studies. The glossary and index section is very helpful for students and can be used as a great resource.

Content appears to be accurate throughout. Being an introductory book, the book is unbiased and straight to the point. The terminology is standard.

The content in textbook is up to date. It will be very easy to update it or make changes at any point in time because of the well-structured contents in the textbook.

The author does a great job of explaining nearly every new term or concept. The book is easy to follow, clear and concise. The graphics are good to follow. The language in the book is easily understandable. I found most instructions in the book to be very detailed and clear for students to follow.

Overall consistency is good. It is consistent in terms of terminology and framework. The writing is straightforward and standardized throughout the text and it makes reading easier.

The authors do a great job of partitioning the text and labeling sections with appropriate headings. The table of contents is well organized and easily divisible into reading sections and it can be assigned at different points within the course.

Organization/Structure/Flow rating: 4

Overall, the topics are arranged in an order that follows natural progression in a statistics course with some exception. They are addressed logically and given adequate coverage.

The text is free of any issues. There are no navigation problems nor any display issues.

The text contains no grammatical errors.

The text is not culturally insensitive or offensive in any way most of time. Some examples might need to consider citing the sources or use differently to reflect current inclusive teaching strategies.

Overall, it's well-written and good recourse to be an introduction to statistical methods. Some materials may not need to be covered in an one-semester course. Various examples and quizzes can be a great recourse for instructor.

Reviewed by Jenna Kowalski, Mathematics Instructor, Anoka-Ramsey Community College on 3/27/18

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks. read more

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks.

Content Accuracy rating: 3

The content of this text is accurate and error-free, based on a random sampling of various pages throughout the text. Several examples included information without formal citation, leading the reader to potential bias and discrimination. These examples should be corrected to reflect current values of inclusive teaching.

The text contains relevant information that is current and will not become outdated in the near future. The statistical formulas and calculations have been used for centuries. The examples are direct applications of the formulas and accurately assess the conceptual knowledge of the reader.

The text is very clear and direct with the language used. The jargon does require a basic mathematical and/or statistical foundation to interpret, but this foundational requirement should be met with course prerequisites and placement testing. Graphs, tables, and visual displays are clearly labeled.

The terminology and framework of the text is consistent. The hyperlinks are working effectively, and the glossary is valuable. Each chapter contains modules that begin with prerequisite information and upcoming learning objectives for mastery.

The modules are clearly defined and can be used in conjunction with other modules, or individually to exemplify a choice topic. With the prerequisite information stated, the reader understands what prior mathematical understanding is required to successfully use the module.

The topics are presented well, but I recommend placing Sampling Distributions, Advanced Graphs, and Research Design ahead of Probability in the text. I think this rearranged version of the index would better align with current Introductory Statistics texts. The structure is very organized with the prerequisite information stated and upcoming learner outcomes highlighted. Each module is well-defined.

Adding an option of returning to the previous page would be of great value to the reader. While progressing through the text systematically, this is not an issue, but when the reader chooses to skip modules and read select pages then returning to the previous state of information is not easily accessible.

No grammatical errors were found while reviewing select pages of this text at random.

Cultural Relevance rating: 3

Several examples contained data that were not formally cited. These examples need to be corrected to reflect current inclusive teaching strategies. For example, one question stated that “while men are XX times more likely to commit murder than women, …” This data should be cited, otherwise the information can be interpreted as biased and offensive.

An included solutions manual for the exercises would be valuable to educators who choose to use this text.

Reviewed by Zaki Kuruppalil, Associate Professor, Ohio University on 2/1/18

This is a comprehensive book on statistical methods, its settings and most importantly the interpretation of the results. With the advent of computers and software’s, complex statistical analysis can be done very easily. But the challenge is the... read more

This is a comprehensive book on statistical methods, its settings and most importantly the interpretation of the results. With the advent of computers and software’s, complex statistical analysis can be done very easily. But the challenge is the knowledge of how to set the case, setting parameters (for example confidence intervals) and knowing its implication on the interpretation of the results. If not done properly this could lead to deceptive inferences, inadvertently or purposely. This book does a great job in explaining the above using many examples and real world case studies. If you are looking for a book to learn and apply statistical methods, this is a great one. I think the author could consider revising the title of the book to reflect the above, as it is more than just an introduction to statistics, may be include the word such as practical guide.

The contents of the book seems accurate. Some plots and calculations were randomly selected and checked for accuracy.

The book topics are up to date and in my opinion, will not be obsolete in the near future. I think the smartest thing the author has done is, not tied the book with any particular software such as minitab or spss . No matter what the software is, standard deviation is calculated the same way as it is always. The only noticeable exception in this case was using the Java Applet for calculating Z values in page 261 and in page 416 an excerpt of SPSS analysis is provided for ANOVA calculations.

The contents and examples cited are clear and explained in simple language. Data analysis and presentation of the results including mathematical calculations, graphical explanation using charts, tables, figures etc are presented with clarity.

Terminology is consistant. Framework for each chapter seems consistent with each chapter beginning with a set of defined topics, and each of the topic divided into modules with each module having a set of learning objectives and prerequisite chapters.

The text book is divided into chapters with each chapter further divided into modules. Each of the modules have detailed learning objectives and prerequisite required. So you can extract a portion of the book and use it as a standalone to teach certain topics or as a learning guide to apply a relevant topic.

Presentation of the topics are well thought and are presented in a logical fashion as if it would be introduced to someone who is learning the contents. However, there are some issues with table of contents and page numbers, for example chapter 17 starts in page 597 not 598. Also some tables and figures does not have a number, for instance the graph shown in page 114 does not have a number. Also it would have been better if the chapter number was included in table and figure identification, for example Figure 4-5 . Also in some cases, for instance page 109, the figures and titles are in two different pages.

No major issues. Only suggestion would be, since each chapter has several modules, any means such as a header to trace back where you are currently, would certainly help.

Grammatical Errors rating: 4

Easy to read and phrased correctly in most cases. Minor grammatical errors such as missing prepositions etc. In some cases the author seems to have the habbit of using a period after the decimal. For instance page 464, 467 etc. For X = 1, Y' = (0.425)(1) + 0.785 = 1.21. For X = 2, Y' = (0.425)(2) + 0.785 = 1.64.

However it contains some statements (even though given as examples) that could be perceived as subjective, which the author could consider citing the sources. For example from page 11: Statistics include numerical facts and figures. For instance: • The largest earthquake measured 9.2 on the Richter scale. • Men are at least 10 times more likely than women to commit murder. • One in every 8 South Africans is HIV positive. • By the year 2020, there will be 15 people aged 65 and over for every new baby born.

Solutions for the exercises would be a great teaching resource to have

Reviewed by Randy Vander Wal, Professor, The Pennsylvania State University on 2/1/18

As a text for an introductory course, standard topics are covered. It was nice to see some topics such as power, sampling, research design and distribution free methods covered, as these are often omitted in abbreviated texts. Each module... read more

As a text for an introductory course, standard topics are covered. It was nice to see some topics such as power, sampling, research design and distribution free methods covered, as these are often omitted in abbreviated texts. Each module introduces the topic, has appropriate graphics, illustration or worked example(s) as appropriate and concluding with many exercises. An instructor’s manual is available by contacting the author. A comprehensive glossary provides definitions for all the major terms and concepts. The case studies give examples of practical applications of statistical analyses. Many of the case studies contain the actual raw data. To note is that the on-line e-book provides several calculators for the essential distributions and tests. These are provided in lieu of printed tables which are not included in the pdf. (Such tables are readily available on the web.)

The content is accurate and error free. Notation is standard and terminology is used accurately, as are the videos and verbal explanations therein. Online links work properly as do all the calculators. The text appears neutral and unbiased in subject and content.

The text achieves contemporary relevance by ending each section with a Statistical Literacy example, drawn from contemporary headlines and issues. Of course, the core topics are time proven. There is no obvious material that may become “dated”.

The text is very readable. While the pdf text may appear “sparse” by absence varied colored and inset boxes, pictures etc., the essential illustrations and descriptions are provided. Meanwhile for this same content the on-line version appears streamlined, uncluttered, enhancing the value of the active links. Moreover, the videos provide nice short segments of “active” instruction that are clear and concise. Despite being a mathematical text, the text is not overly burdened by formulas and numbers but rather has “readable feel”.

This terminology and symbol use are consistent throughout the text and with common use in the field. The pdf text and online version are also consistent by content, but with the online e-book offering much greater functionality.

The chapters and topics may be used in a selective manner. Certain chapters have no pre-requisite chapter and in all cases, those required are listed at the beginning of each module. It would be straightforward to select portions of the text and reorganize as needed. The online version is highly modular offering students both ease of navigation and selection of topics.

Chapter topics are arranged appropriately. In an introductory statistics course, there is a logical flow given the buildup to the normal distribution, concept of sampling distributions, confidence intervals, hypothesis testing, regression and additional parametric and non-parametric tests. The normal distribution is central to an introductory course. Necessary precursor topics are covered in this text, while its use in significance and hypothesis testing follow, and thereafter more advanced topics, including multi-factor ANOVA.

Each chapter is structured with several modules, each beginning with pre-requisite chapter(s), learning objectives and concluding with Statistical Literacy sections providing a self-check question addressing the core concept, along with answer, followed by an extensive problem set. The clear and concise learning objectives will be of benefit to students and the course instructor. No solutions or answer key is provided to students. An instructor’s manual is available by request.

The on-line interface works well. In fact, I was pleasantly surprised by its options and functionality. The pdf appears somewhat sparse by comparison to publisher texts, lacking pictures, colored boxes, etc. But the on-line version has many active links providing definitions and graphic illustrations for key terms and topics. This can really facilitate learning as making such “refreshers” integral to the new material. Most sections also have short videos that are professionally done, with narration and smooth graphics. In this way, the text is interactive and flexible, offering varied tools for students. To note is that the interactive e-book works for both IOS and OS X.

The text in pdf form appeared to free of grammatical errors, as did the on-line version, text, graphics and videos.

This text contains no culturally insensitive or offensive content. The focus of the text is on concepts and explanation.

The text would be a great resource for students. The full content would be ambitious for a 1-semester course, such use would be unlikely. The text is clearly geared towards students with no statistics background nor calculus. The text could be used in two styles of course. For 1st year students early chapters on graphs and distributions would be the starting point, omitting later chapters on Chi-square, transformations, distribution-free and size effect chapters. Alternatively, for upper level students the introductory chapters could be bypassed with the latter chapters then covered to completion.

This text adopts a descriptive style of presentation with topics well and fully explained, much like the “Dummy series”. For this, it may seem a bit “wordy”, but this can well serve students and notably it complements powerpoint slides that are generally sparse on written content. This text could be used as the primary text, for regular lectures, or as reference for a “flipped” class. The e-book videos are an enabling tool if this approach is adopted.

Reviewed by David jabon, Associate Professor, DePaul University on 8/15/17

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary. read more

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary.

The material is completely accurate. There are no errors. The terminology is standard with one exception: the book calls what most people call the interquartile range, the H-spread in a number of places. Ideally, the term "interquartile range" would be used in place of every reference to "H-spread." "Interquartile range" is simply a better, more descriptive term of the concept that it describes. It is also more commonly used nowadays.

This book came out a number of years ago, but the material is still up to date. Some more recent case studies have been added.

The writing is very clear. There are also videos for almost every section. The section on boxplots uses a lot of technical terms that I don't find are very helpful for my students (hinge, H-spread, upper adjacent value).

The text is internally consistent with one exception that I noted (the use of the synonymous words "H-spread" and "interquartile range").

The text book is brokenly into very short sections, almost to a fault. Each section is at most two pages long. However at the end of each of these sections there are a few multiple choice questions to test yourself. These questions are a very appealing feature of the text.

The organization, in particular the ordering of the topics, is rather standard with a few exceptions. Boxplots are introduced in Chapter II before the discussion of measures of center and dispersion. Most books introduce them as part of discussion of summaries of data using measure of center and dispersion. Some statistics instructors may not like the way the text lumps all of the sampling distributions in a single chapter (sampling distribution of mean, sampling distribution for the difference of means, sampling distribution of a proportion, sampling distribution of r). I have tried this approach, and I now like this approach. But it is a very challenging chapter for students.

The book's interface has no features that distracted me. Overall the text is very clean and spare, with no additional distracting visual elements.

The book contains no grammatical errors.

The book's cultural relevance comes out in the case studies. As of this writing there are 33 such case studies, and they cover a wide range of issues from health to racial, ethnic, and gender disparity.

Each chapter as a nice set of exercises with selected answers. The thirty three case studies are excellent and can be supplement with some other online case studies. An instructor's manual and PowerPoint slides can be obtained by emailing the author. There are direct links to online simulations within the text. This text is very high quality textbook in every way.

Table of Contents

• 1. Introduction
• 2. Graphing Distributions
• 3. Summarizing Distributions
• 4. Describing Bivariate Data
• 5. Probability
• 6. Research Design
• 7. Normal Distributions
• 8. Advanced Graphs
• 9. Sampling Distributions
• 10. Estimation
• 11. Logic of Hypothesis Testing
• 12. Testing Means
• 14. Regression
• 15. Analysis of Variance
• 16. Transformations
• 17. Chi Square
• 18. Distribution-Free Tests
• 19. Effect Size
• 20. Case Studies
• 21. Glossary

Ancillary Material

• Ancillary materials are available by contacting the author or publisher .

About the Book

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

About the Contributors

David Lane is an Associate Professor in the Departments of Psychology, Statistics, and Management at the Rice University. Lane is the principal developer of this resource although many others have made substantial contributions. This site was developed at Rice University, University of Houston-Clear Lake, and Tufts University.

Contribute to this Page

Sample Assignments

DOWNLOAD PAST SAMPLE ASSIGNMENTS FOR FREE

Statistics Sample Tasks ------>

Statistics Assignment Solutions Prepared By Our Experts

The below samples have been prepared by our tutors to give you an understanding of the quality of the service that we provide.

• Statistics Assignment Samples with Solution

Sample Content

Attached files.

• Math Article

Statistics Questions

Statistics questions based on CBSE syllabus and NCERT guidelines are provided here for students. These questions cover the syllabus of Statistics concept for Class 9, 10 and 11. Practising these problems will help students to score better on this topic. Also, it will help them to participate in competitive exams. Also, find probability questions here at BYJU’S.

Definition : Statistics is a branch of mathematics concerned with collecting meaningful data or information.

Also, read: Statistics

Questions on Statistics with Answers

1. Give any two examples of collecting data from day-to-day life.

A. Increase in population of our country in the last two decades.

B. Number of tables and chairs in a classroom

2. If marks obtained by students in a class test is given as per below:

55 36 95 73 60 42 25 78 75 62

Then arrange the marks from lowest to highest.

Solution: We need to arrange the marks obtained by each student in ascending order:

25 36 42 55 60 62 73 75 78 95

3. Check the following frequency distribution table, consisting of weights of 38 students of a class:

(i) What is class-interval for classes 31 – 35?

(ii) How many students are there in the range of 41-45 kgs?

(i) Class interval = Upper class limit – lower class limit

(ii) For the 41-45 range, there are 14 students.

4. A family with a monthly income of ` 20,000 had planned the following expenditures per month under various heads:

Draw a bar graph for the data above.

5. In a city, the weekly observations made in a study on the cost of the living index are given in the following table:

Draw a frequency polygon for the data above.

Solution: Class- mark = (Upper class limit-lower class limit)/2 = (150 + 140)/2 = 290/2 = 145

Thus we can create a new table with class-mark.

Now with these class marks we can plot the frequency polygon as shown below.

6. Consider a small unit of a factory where there are 5 employees : a supervisor and four labourers. The workers earn a salary of Rs. 5,000 per month each while the supervisor gets Rs. 15,000 per month. Calculate the mean, median and mode of the salaries.

Mean = (5000 + 5000 + 5000 + 5000 + 15000)/5 = 35000/5 = 7000

So, the mean salary is Rs. 7000 per month

To obtain the median, let us arrange the salaries in ascending order:

5000, 5000, 5000, 5000, 15000

Median = (n+1)/2 = (5+1)/2 = 6/2 = 3rd observation

Median = Rs. 5000/-

Mode = Number of times an observation is repeated = Rs.5000/-

7. The distribution in the table below shows the number of wickets taken by bowlers in one-day cricket matches. Find the mean number of wickets using the correct method. What does the mean signify?

Solution: Here, the class size varies, and the class marks (x i ) are large. Apply the step deviation method for a = 200 and h = 20.

So, ʉ = -106/45

x̄ = 200+20(-106/45) = 200 – 47.11 = 152.89

Hence, on an average, the number of wickets taken by these 45 bowlers in one-day cricket is 152.89.

8. A survey conducted on 20 houses in an area by a group of people resulted in the subsequent frequency table for the number of family members in a house:

Find the mode of this data.

Solution: Here the maximum class frequency = 8,

Class corresponding to this frequency = 3 – 5.

So, the modal class = 3 – 5.

Modal class = 3 – 5, lower limit (l) of modal class = 3, class size (h) = 2

Frequency (f 1 ) of the modal class = 8

Frequency (f 0 ) of class preceding the modal class = 7,

Frequency (f 2 ) of class succeeding the modal class = 2.

Now, let us put these values in the formula :

Therefore, the mode of the data above is 3.286.

9. A survey regarding the heights (in cm) of 51 girls of Class X of a school was conducted and the following data were obtained:

Find the median height.

Solution: Observe the below table:

n/2 = 51/2 = 25.5

This observation lies in the class 145 – 150.

l (the lower limit) = 145,

cf (the cumulative frequency of the class preceding 145 – 150) = 11

f (the frequency of the median class 145 – 150) = 18,

h (the class size) = 5

Using the formula,

So, the median height of the girls is 149.03 cm.

Related Articles:

• Important Questions Class 9 Maths Chapter 14 Statistics
• Important Questions Class 10 Maths Chapter 14 Statistics
• Important Questions Class 11 Maths Chapter 15 Statistics

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Request OTP on Voice Call

Post My Comment

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

• Write My Statistics Paper
• Jamovi Assignment Help
• Business Statistics Assignment Help
• RapidMiner Assignment Help
• Econometric Software Assignment Help
• Econometrics Assignment Help
• Game Theory Assignment Help
• Take My Statistics Exam
• Statistics Assignment Helper
• Statistics Research Paper Assignment Help
• Do My Statistics Assignment
• Pay Someone to Do My Statistics Assignment
• Professional Statistics Assignment Writers
• Need Help with My Statistics Assignment
• Hire Someone to Do My Statistics Assignment
• Statistics Assignment Experts
• Statistics Midterm Assignment Help
• Statistics Capstone Project Help
• Urgent Statistics Assignment Help
• Take My Statistics Quiz
• Professional Statistics Assignment Help
• Statistics Assignment Writers
• Best Statistics Assignment Help
• Affordable Statistics Assignment Help
• Final Year Statistics Project Help
• Statistics Coursework Help
• Reliable Statistics Assignment Help
• Take My Statistics Test
• Custom Statistics Assignment Help
• SPSS Assignment Help
• STATA Assignment Help
• Statistix Assignment Help
• Calculus Assignment Help
• Statistical Hypothesis Formulation Assignment Help
• LabVIEW Assignment Help
• LISREL Assignment Help
• Minitab Assignment Help
• Analytica Software Assignment Help
• Statistica Assignment Help
• Design Expert Assignment Help
• Orange Assignment Help
• KNIME Assignment Help
• WinBUGS Assignment Help
• SAS Assignment Help
• Excel Assignment Help
• JASP Assignment Help
• JMP Assignment Help
• Alteryx Assignment Help
• Statistical Software Assignment Help
• GRETL Assignment Help
• Apache Hadoop Assignment Help
• XLSTAT Assignment Help
• Linear Algebra Assignment Help
• Data Analysis Assignment Help
• Finance Assignment Help
• ANOVA Assignment Help
• Black Scholes Assignment Help
• Experimental Design Assignment Help
• CNN (Convolutional Neural Network) Assignment Help
• Statistical Tests and Measures Assignment Help
• Multiple Linear Regression Assignment Help
• Correlation Analysis Assignment Help
• Data Classification Assignment Help
• Decision Making Assignment Help
• 2D Geometry Assignment Help
• Distribution Theory Assignment Help
• Decision Theory Assignment Help
• Data Manipulation Assignment Help
• Binomial Distributions Assignment Help
• Linear Regression Assignment Help
• Statistical Inference Assignment Help
• Structural Equation Modeling (SEM) Assignment Help
• Numerical Methods Assignment Help
• Markov Processes Assignment Help
• Non-Parametric Tests Assignment Help
• Multivariate Statistics Assignment Help
• Data Flow Diagram Assignment Help
• Bivariate Normal Distribution Assignment Help
• Matrix Operations Assignment Help
• CFA Assignment Help
• Mathematical Methods Assignment Help
• Probability Assignment Help
• Kalman Filter Assignment Help
• Kruskal-Wallis Test Assignment Help
• Stochastic Processes Assignment Help
• Chi-Square Test Assignment Help
• Six Sigma Assignment Help
• Hypothesis Testing Assignment Help
• GAUSS Assignment Help
• GARCH Models Assignment Help
• Simple Random Sampling Assignment Help
• GEE Models Assignment Help
• Principal Component Analysis Assignment Help
• Multicollinearity Assignment Help
• Linear Discriminant Assignment Help
• Logistic Regression Assignment Help
• Survival Analysis Assignment Help
• Nonparametric Statistics Assignment Help
• Poisson Process Assignment Help
• Cluster Analysis Assignment Help
• ARIMA Models Assignment Help
• Measures of central tendency
• Time Series Analysis Assignment Help
• Factor Analysis Assignment Help
• Regression Analysis Assignment Help
• Survey Methodology Assignment Help
• Statistical Modeling Assignment Help
• Survey Design Assignment Help
• Linear Programming Assignment Help
• Confidence Interval Assignment Help
• Quantitative Methods Assignment Help
• Essential Topics to Master Before Starting an SPSS Assignment

Submit Your SPSS Assignment

Understanding the basics of spss, data entry and data import, descriptive statistics, hypothesis testing, correlation and regression, data visualization, data transformation and variable recoding.

Understanding the basics of SPSS is crucial for any data analysis project. SPSS (Statistical Package for the Social Sciences) is a powerful software widely used in various fields to perform statistical analyses and interpret data. It provides an intuitive interface, making it accessible to both beginners and experienced researchers. By learning the fundamentals of data entry, importing, and cleaning, users can ensure accurate and reliable analyses. Moreover, mastering descriptive statistics, hypothesis testing, and data visualization will enable researchers to draw meaningful insights from their data. This foundational knowledge sets the stage for more advanced statistical analyses and a successful SPSS journey.

The following topics are essential to know:

Data entry and data import are critical steps in the SPSS workflow. Properly organizing and entering data is essential for accurate analysis and valid results. SPSS offers various methods to input data, including manual entry or importing from external sources like Excel or CSV files. Understanding how to handle missing data and outliers during this process is crucial to ensure data integrity. Additionally, knowing how to label variables and assign value labels improves data clarity and interpretation. By mastering data entry and import, researchers can avoid data errors, save time, and lay a solid foundation for a successful SPSS assignment.

Some of the assignments you can expect on data entry and data import include:

• Data Entry Accuracy Assessment: To solve a data entry accuracy assessment assignment, carefully enter the provided dataset into SPSS while minimizing errors. Double-check the data for accuracy and correct any mistakes. Use validation techniques such as cross-referencing with the original data source. Analyze any discrepancies and document your approach to ensure transparency. This exercise helps improve data entry skills and emphasizes the importance of accurate data handling for reliable statistical analysis.
• Data Import and Cleaning: To solve a data import and cleaning assignment, start by importing the dataset into SPSS from various file formats (Excel, CSV). Address missing values, duplicates, and outliers. Check data consistency and validity. Employ functions for data cleaning, like recoding variables or imputing missing values. Document your steps clearly. Lastly, validate the cleaned dataset for accuracy and usability before proceeding with any further analysis.
• Merging Datasets: To solve an assignment on merging datasets in SPSS, follow these steps. First, ensure datasets have a common identifier (e.g., ID). Use the "Merge Files" function, select appropriate merge type (e.g., inner, outer), and identify the matching variable. Check for duplicate records and resolve inconsistencies. Use the "Split File" option for separate analyses. Validate the merged dataset by comparing results with the original files. A successful assignment requires understanding data relationships and using SPSS tools accurately for a comprehensive analysis.
• Longitudinal Data Handling: To solve an assignment on longitudinal data handling, first, understand the dataset's structure and time points. Organize the data in SPSS, ensuring it's in the appropriate format (wide or long). Use the "Restructure Data" or "Split File" functions to perform time-series analysis. Apply statistical techniques such as repeated measures ANOVA or growth curve modeling to examine trends and changes over time. Finally, interpret and present the findings, showcasing a clear understanding of the data's longitudinal nature and demonstrating analytical skills.

Descriptive statistics play a fundamental role in data analysis by providing a concise summary of the main features within a dataset. These statistics, including measures like mean, median, mode, standard deviation, and variance, offer valuable insights into the central tendency, spread, and distribution of the data. Understanding descriptive statistics in SPSS allows researchers to gain a clear understanding of their data before moving on to more complex analyses. Additionally, visual representations, such as histograms and box plots, help researchers identify patterns and outliers, making it easier to make informed decisions and draw meaningful conclusions from the data at hand.

Here are the types of assignments you will get on descriptive statistics and how you can solve them:

• Central Tendency Assignment: To solve a central tendency assignment, import the dataset into SPSS, calculate the mean, median, and mode using the "Descriptive" option, and interpret the results. The mean represents the average, the median is the middle value, and the mode is the most frequent value in the dataset, providing insights into the central tendencies of the data.
• Measures of Dispersion Assignment: To solve a measures of dispersion assignment, import the dataset into SPSS, then calculate the range, standard deviation, and variance using the "Descriptive" option. Interpret the results to understand the spread of the data, identifying the variability and distribution characteristics.
• Frequency Distribution Assignment: To solve a frequency distribution assignment, import the dataset into SPSS, then use the "Frequencies" option to generate frequency tables for the variables of interest. Additionally, create histograms to visualize the distribution. Analyze the frequency tables and histograms to identify patterns and trends in the data.
• Correlation Assignment: To solve a correlation assignment, first, import the dataset into SPSS. Choose the variables you want to explore for correlation. Use the "Correlations" option to calculate correlation coefficients. Interpret the results to determine the strength and direction of the relationship between the variables, considering statistical significance using p-values.

Hypothesis testing is a fundamental concept in statistics and plays a pivotal role in research and decision-making processes. In SPSS, researchers can examine whether their hypotheses are supported or refuted based on empirical evidence. By setting up null and alternative hypotheses and using appropriate statistical tests like t-tests or ANOVA, analysts can draw conclusions about the population from a sample. Understanding p-values, significance levels, and the correct interpretation of results are essential to avoid drawing incorrect conclusions. Hypothesis testing in SPSS empowers researchers to make data-driven decisions and contributes to the validity and reliability of their research findings.

Types of Hypothesis Testing Assignments:

• One-Sample T-Test Assignment: In this assignment, you are given a dataset with a single sample, and you need to test whether the sample mean differs significantly from a hypothesized value. Use SPSS to perform a one-sample t-test. Enter the data, set the null hypothesis, select the t-test option, and interpret the result based on the p-value and significance level.
• Independent Samples T-Test Assignment: In this assignment, you are provided with two separate datasets representing independent groups, and you need to determine if there is a significant difference in the means of the two groups. Input the data, set the null hypothesis, select the t-test option, and interpret the outcome based on the p-value and significance level.
• Paired Samples T-Test Assignment: In this assignment, you are given two related datasets, and your task is to examine if there is a significant difference between the means of the paired samples. Use SPSS to execute a paired samples t-test. Enter the paired data, set the null hypothesis, select the t-test option, and interpret the results using the p-value and significance level.
• One-Way ANOVA Assignment: In this assignment, you are provided with a dataset containing multiple groups, and you need to ascertain if there are significant differences in means across those groups. Employ SPSS to perform a one-way ANOVA. Enter the data, set the null hypothesis, select the ANOVA option, and interpret the result based on the p-value and significance level. Additionally, post-hoc tests may be required to identify specific group differences.

Correlation measures the relationship between two or more variables, while regression predicts the value of a dependent variable based on one or more independent variables. These topics are often encountered in research and data analysis. Knowing how to perform correlation and regression analyses in SPSS will enable you to explore relationships and make predictions from your data.

• Simple Correlation Analysis Assignment: For this assignment, calculate and interpret the correlation coefficient between two variables using SPSS. Identify the strength and direction of the relationship and present your findings in a clear and concise manner.
• Multiple Regression Assignment: In this task, perform multiple regression analysis in SPSS to predict a dependent variable based on two or more independent variables. Select relevant variables, run the regression, and interpret the coefficients to draw meaningful conclusions.
• Correlation and Regression Comparison Assignment: Compare and contrast correlation and regression analyses in SPSS. Explain their purposes, assumptions, and interpretations. Provide examples to demonstrate their applications in different scenarios.
• Real-Life Data Analysis Assignment: Obtain a dataset with variables suitable for correlation and regression analysis. Clean the data, perform the appropriate analysis in SPSS, and interpret the results. Discuss the practical implications of the findings in a real-world context.

Data visualization plays a pivotal role in understanding complex datasets and communicating insights effectively. SPSS offers a wide range of visualization options, such as histograms, scatter plots, and bar charts, allowing researchers to present data in a visually engaging manner. By choosing the appropriate charts, researchers can identify patterns, trends, and outliers, making it easier to draw conclusions from the data. Furthermore, visualizations aid in conveying findings to a broader audience, making complex statistical information more accessible and comprehensible. A skillful use of data visualization in SPSS enhances the clarity and impact of research results, thereby strengthening the overall research narrative.

Types of data visualization assignments:

• Creating Descriptive Visualizations: In this type of assignment, you may be asked to generate descriptive visualizations for a given dataset using SPSS. Start by importing the data and exploring its variables. Use appropriate chart types such as histograms, bar charts, and pie charts to visualize the distribution of categorical and numerical variables. Customize the visuals by adding labels, titles, and color schemes to improve clarity. For numerical data, consider box plots and scatter plots to identify outliers and patterns. Present the visualizations along with a brief interpretation of the main insights.
• Comparative Visualizations: In a comparative visualization assignment, you might need to compare two or more groups or variables. Use grouped bar charts, stacked bar charts, or line graphs to demonstrate the differences between the groups. Apply color coding and legends to make the visualizations more informative. For more advanced analyses, consider using heatmaps or radar charts to display multivariate comparisons. Explain the key findings and any significant trends or patterns observed in the data.
• Time-Series Visualizations: Time-series visualizations involve displaying data points over time. Use line graphs or area charts to represent the trends and changes in the data over specific time intervals. Pay attention to the x-axis labels and format to ensure the time is displayed accurately. Utilize different line styles or colors for multiple time series. If applicable, add annotations or callouts to highlight important events or occurrences during the time period. Analyze the visualizations to draw conclusions about any temporal patterns or fluctuations.
• Geospatial Visualizations: In geospatial visualization assignments, you will be working with spatial data and representing it on maps. Import the geographic data into SPSS and link it with your dataset. Use choropleth maps to display numerical data for different regions or territories. You can also use bubble maps to show variations in data based on the size of the bubbles in different locations. Customize the map legend, color scales, and data ranges to enhance the visualization's clarity. Analyze the geospatial visualizations to draw insights about spatial patterns and regional differences in the data.

Data transformation and variable recoding are vital skills in SPSS for preparing data for analysis. Data transformation involves converting variables into different formats or scales, such as logarithmic or square root transformations, to meet statistical assumptions. Variable recoding allows researchers to combine or modify existing variables, simplifying the analysis. These techniques are useful when dealing with skewed data or categorical variables. By mastering these methods, researchers can enhance the accuracy and reliability of their analyses and derive more insightful results from their data.

• Log Transformation for Skewed Data: To solve an assignment on log transformation for skewed data, first, identify the skewed variable. Calculate the natural logarithm (ln) of each value in the variable to create a new transformed variable. This process helps normalize the data, making it suitable for analysis that requires normally distributed data.
• Recoding Categorical Variables: To solve an assignment on recoding categorical variables, start by identifying the specific categorical variable and the desired outcome (e.g., binary or multi-category recoding). Create a new variable, assign codes to each category accordingly, and recode the data. Validate the recoded variable's accuracy and use it in subsequent analyses for simplified interpretations.
• Standardization of Variables: To solve an assignment on standardization of variables, calculate the mean and standard deviation for each variable. For each data point, minus the mean and divide the answer by the standard deviation. This process will transform the variables into a common scale with a mean of 0 and a standard deviation of 1, allowing for fair comparisons and unbiased analysis.
• Binning Continuous Variables: To solve an assignment on binning continuous variables, first, determine suitable bin intervals based on the data's distribution and context. Then, divide the range of the continuous variable into these intervals and create a new categorical variable. Assign data points to the corresponding bins, facilitating analysis and interpretation in distinct groups.

Mastering the essential topics in SPSS and knowing how to approach SPSS assignments will empower you to handle various data analysis tasks confidently. By understanding the basics of SPSS, data entry, hypothesis testing, correlation, regression, data visualization, and data transformation, you will be well-prepared to tackle a wide range of statistical problems. Through practice and hands-on experience with SPSS, you can enhance your analytical skills and become proficient in using this powerful statistical software for research and data analysis.

Similar Blogs

Our popular services.

[email protected]

• Sample Assignments
• Analytica Assignment Help
• AWS Assignment Help
• ConnectMath Assignment Help
• ERP Assignment Help
• EViews Assignment Help
• Excel Assignment Help
• Information Technology Assignment Help
• JMP Assignment Help
• Keras Assignment Help Online
• LabVIEW Assignment Help
• LISREL Assignment Help
• MATLAB Assignment Help
• MegaStat Assignment Help
• Minitab Assignment Help
• MYOB Assignment Help
• Power BI Assignment Help
• Programming Assignment Help
• Python Assignment Help
• R Programming Assignment Help
• R Shiny Assignment Help
• SAS Assignment Help
• Software Engineering Assignment Help
• SPSS Amos- SEM Assignment Help
• SPSS Assignment Help
• SQL Assignment Help
• STATA Assignment Help
• Statistics Assignment Experts
• Statistix Assignment Help
• Tableau Assignment Help
• TensorFlow Assignment Help
• XLSTAT Assignment Help
• Advanced Probability Theory Assignment Help
• ANOVA Assignment Help
• Applied Statistics Assignment Help
• Bayesian Statistics Assignment Help
• Black Scholes Theory Assignment Help
• Blockchain Technology Assignment Help
• C++ Assignment Help
• Calculus Assignment Help
• Chi-square Testing Assignment Help
• Cluster Analysis Assignment Help
• CNN Assignment Help
• Computer Architecture Assignment Help
• Confidence Intervals Assignment Help
• Control Charts Assignment Help
• Correlation Analysis Assignment Help
• Cyber Security Assignment Help
• Data Analysis Assignment Help
• Data Classification Assignment Help
• Database Management Assignment Help
• Decision Theory Assignment Help
• Decision Tree Assignment Help
• Descriptive Statistics Assignment Help
• Distribution Theory Assignment Help
• Factor Analysis Assignment Help
• Game Theory Assignment Help
• Hypothesis Testing Assignment Help
• Kalman & Particle Filter Assignment Help
• Linear Algebra Assignment Help
• Linear Discriminant Analysis Assignment Help
• Linear Programming Assignment Help
• Logistics Regression Assignment Help
• Markov Processes Assignment Help
• Mathematical Methods Assignment Help
• MATLAB GUI Assignment Help
• Monte Carlo simulation Assignment Help
• Multivariate Analysis Assignment Help
• Multivariate Statistics Assignment Help
• Neural Networks Assignment Help
• Nonparametric Tests Assignment Help
• Numerical Methods in MATLAB
• Operating System Assignment Help
• Principal Component Analysis Assignment Help
• Probability Assignment Help
• Probability Distributions Assignment Help
• Psychology Statistics Assignment Help
• Regression Analysis Assignment Help
• Sampling Assignment Help
• Statistical Inference Assignment Help
• Stochastic Processes Assignment Help
• Survey Methodology Assignment Help
• Time Series Assignment Help
• Time Series Homework Help
• Artificial Intelligence Assignment Help
• Backpropagation Assignment Help
• Big Data Assignment Help
• Business Analytics Assignment Help
• C Programming Assignment Help
• Chatbot Assignment Help
• Clinical Psychology Assignment Help
• Clinical Trials Assignment Help
• Coding Assignment Help
• Computer Networking Assignment Help
• Computer Science Assignment Help
• Computer Vision Assignment Help Online
• Consumer Behavior Assignment Help
• Control Systems Using MATLAB
• Data Analytics Assignment Help
• Data Flow Diagram Assignment Help
• Data Mining Assignment Help
• Data Science Assignment Help
• Deep Learning Assignment Help
• Derivatives Assignment Help
• Digital Signal Processing in MATLAB
• Econometrics Assignment Help
• Finance Assignment Help
• Finance Insurance Assignment Help
• Financial Risk Analysis Assignment Help
• Financial Statistics Assignment Help
• Fixed Income Markets Assignment Help
• Flask Assignment Help
• Forecasting Financial Time Series
• Image Processing in MATLAB
• Machine Learning Assignment Help
• Math Assignment Help
• MATLAB in Computing
• MyStatLab Help
• Natural Language Processing Assignment Help
• Network Design in MATLAB
• Operations Research Assignment Help
• Project Management Assignment Help
• Quantitative Psychology Assignment Help
• Random Forest Assignment Help
• Reinforcement Learning Assignment Help
• Statistics Dissertation Help
• Supervised Learning Assignment Help
• Support Vector Machine Assignment Help
• Take My R Programming Exam
• Unsupervised Learning Assignment Help
• ANOVA Homework Help
• Computer Science Homework Help
• Data Mining Homework Help
• Java Homework Help
• Live Exam Help
• MyMathlab Quiz Help
• Neural Networks Homework Help
• Online Calculus Exam Help
• Online Computer Engineering Exam Help
• Online Math Exam Help
• Online Programming Exam Helper
• Online Python Exam Help
• Online Statistics Exam Helper
• Pay Someone To Take Statistics Exam
• Proctored Exam Help
• R Programming Homework Help
• Statistics Homework Help
• Take My GED Test Online
• Take My Online Exam
• Take My Psychology Quiz
• Take My Statistics Quiz
• Take my Statistics Test
• Accounting Assignment Writers in the US
• Accounting Dissertation Help
• Accounting Research Paper Help
• Activity Based Accounting Assignment Help
• Auditing Assignment Help
• Balance Sheet Analysis Assignment Help
• Behavioral Finance Assignment Help
• Business Valuation Assignment Help
• Capital Budgeting Assignment Help
• Cost Accounting Assignment Help
• Demand Forecast Assignment Help
• Economics Cost Curves Assignment Help
• Financial Accounting Assignment Help
• Financial Accounting Exam Help
• Financial Reporting Assignment Help
• Financial Statement Analysis Assignment Help
• Forensic Accounting Assignment Help
• Fund Accounting Assignment Help
• International Finance Assignment Help
• Managerial Accounting Assignment Help
• Mergers and Acquisitions Assignment Help
• Online Economics Exam Help
• Online Finance Exam Help
• Public Economics Assignment Help
• Solve My Accounting Paper
• Statistics Research Paper Help
• Take My Cost Accounting Exam
• Take My Managerial Accounting Exam
• Tax Accounting Assignment Help
• Python Assignment Help Australia
• Python Assignment Help Canada
• Python Assignment Help UK
• Python Assignment Help USA
• Statistics Assignment Help Australia
• Statistics Assignment Help Canada
• Statistics Assignment Help Hong Kong
• Statistics Assignment Help Ireland
• Statistics Assignment Help New Zealand
• Statistics Assignment Help Qatar
• Statistics Assignment Help Saudi Arabia
• Statistics Assignment Help Singapore
• Statistics Assignment Help UK
• Statistics Assignment Help USA
• Statistics Project Help

Can't read the image? click here to refresh.

Why Choose The Programming Assignment Help?

On Time Delivery

Plagiarism Free Service

24/7 Support

Affordable Pricing

PhD Holder Experts

100% Confidentiality

Samples Assignments

Our programming experts have prepared some sample assignment solutions in order to demonstrate the quality of our work. All the solutions have been prepared by following a simplistic approach and include well commented, executable codes. These sample solutions reflect the in-depth expertise and experience of our experts

Disclaimer: These sample assignment solutions are only to be considered for reference. Any previous assignments prepared by our experts are not included in the sample solutions

Machine Learning

R Programming

Data Mining

• A Comprehensive Guide to Writing Assignments using SPSS

A Comprehensive Guide to Writing Assignments Using SPSS

Understand the basics of spss, learn how to input data into spss, know how to use the different analysis tools in spss, understand how to interpret the output from spss, plan your assignment, define your research question, select an appropriate data set, decide on the analysis tools, write your assignment, introduction, literature review, methodology, edit and revise your assignment.

• Clarity: Make sure your writing is understandable and clear.
• Grammar and spelling: Make sure there are no grammatical or spelling errors in your writing.
• Logic and organization: Make sure your assignment is logically structured and well-organized.
• Formatting: Make sure your assignment complies with the formatting requirements specified by your teacher.

Post a comment...

A comprehensive guide to writing assignments using spss submit your assignment, attached files.

• Claim 25% OFF on First Order Online Grab Now

Assignment Help & Homework

Writers for Hire | MBA, Ph.D & Masters

• Statistics Assignment Sample
• Free Assignment Samples

Statistics is the science that concern with the collecting, analyzing, presenting & evaluating of data. Governmental needs for evaluation data and information about various economic activities provided much of the early incentive for the field of statistics. The need to turn the large amounts of data available in many applied fields into useful information has stimulated both theoretical and practical developments in statistics.

Also Read: Free Assignment Samples in Australia

Online Statistics Assignment Sample

Looking for expert help in Statistics assignments, then Statistics Assignment Sample and Examples for students are available at Assignmenthelpaus.com . Our Statistics Assignment Samples are not just a well-written paper, but it is one of your steps to get the best quality assignments. Our experts conduct in-depth research for each Statistics Assignment Sample and follow university assignments layout. They gather information from reliable sources. So, our experts provide informative Statistics Assignment Sample and Example for Students. These samples are a good source of reference to best quality assignments to help students understand their subject’s assignment better. Contact us to place your order now.

Get start Today

The Field of Statistics in Divided into two Major Parts:

1 Descriptive 2 Inferential

1 Descriptive: Descriptive Statistics is the way to summarized and evaluate the collected data so that a meaningful pattern formed. But this area is narrowed down for concluding only those information that we have collected or presented to us. Hence, applying descriptive statistics requires two types of statistical ideas:

• Graphically (histogram, bar graph etc.)
• Numerically (Mean, Median, Mode)

2 Inferential: Inferential is one of the two major parts of statistics. Inferential statistics used random samples of data taken from population to describe and make inferences about the population. You can use the information about parametric and nonparametric tests. Both of these types of analyses are used to determine whether the results are likely to be changed in variable value.

Get Experts Help & Free Assignment Samples at Assignmenthelpaus.com Hire an Experts

List Of Topics That Require Statistic Assignment Writing:

• Quantitative analysis
• Biostatistics assignment
• Descriptive statistics
• Probability distributions
• MATLAB assignment
• Reliability theory
• SPSS assignment
• Binomial distribution

Why Students First Choice Assignmenthelpaus.com for their Academic Help

We are available in any concern in all over Australia to Provide you Assignment Writing help as well as statistics assignment solutions. Doesn’t matter are you fresher or Final year Student. Our First Priority is students don’t suffer for their Academic Assignment help in Australia. here are we make sure that:

• Top Quality Assignment
• 24*7 Online Executive support
• 100% original & plagiarism-free Work
• Enjoy Personalized Assignment Help At Most Reliable Price

Is Assignmenthelpaus the Reliable Assignment help Provider in Australia?

Here are the reasons,  please check out our Valuable Customers review>>>>>>>>>

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

An Easy Introduction to Statistical Significance (With Examples)

Published on January 7, 2021 by Pritha Bhandari . Revised on June 22, 2023.

If a result is statistically significant , that means it’s unlikely to be explained solely by chance or random factors. In other words, a statistically significant result has a very low chance of occurring if there were no true effect in a research study.

The p value , or probability value, tells you the statistical significance of a finding. In most studies, a p value of 0.05 or less is considered statistically significant, but this threshold can also be set higher or lower.

Table of contents

How do you test for statistical significance, what is a significance level, problems with relying on statistical significance, other types of significance in research, other interesting articles, frequently asked questions about statistical significance.

In quantitative research , data are analyzed through null hypothesis significance testing, or hypothesis testing. This is a formal procedure for assessing whether a relationship between variables or a difference between groups is statistically significant.

Null and alternative hypotheses

To begin, research predictions are rephrased into two main hypotheses: the null and alternative hypothesis .

• A null hypothesis ( H 0 ) always predicts no true effect, no relationship between variables , or no difference between groups.
• An alternative hypothesis ( H a or H 1 ) states your main prediction of a true effect, a relationship between variables, or a difference between groups.

Hypothesis testin g always starts with the assumption that the null hypothesis is true. Using this procedure, you can assess the likelihood (probability) of obtaining your results under this assumption. Based on the outcome of the test, you can reject or retain the null hypothesis.

• H 0 : There is no difference in happiness between actively smiling and not smiling.
• H a : Actively smiling leads to more happiness than not smiling.

Test statistics and p values

Every statistical test produces:

• A test statistic that indicates how closely your data match the null hypothesis.
• A corresponding p value that tells you the probability of obtaining this result if the null hypothesis is true.

The p value determines statistical significance. An extremely low p value indicates high statistical significance, while a high p value means low or no statistical significance.

Next, you perform a t test to see whether actively smiling leads to more happiness. Using the difference in average happiness between the two groups, you calculate:

• a t value (the test statistic) that tells you how much the sample data differs from the null hypothesis,
• a p value showing the likelihood of finding this result if the null hypothesis is true.

Prevent plagiarism. Run a free check.

The significance level , or alpha (α), is a value that the researcher sets in advance as the threshold for statistical significance. It is the maximum risk of making a false positive conclusion ( Type I error ) that you are willing to accept .

In a hypothesis test, the  p value is compared to the significance level to decide whether to reject the null hypothesis.

• If the p value is  higher than the significance level, the null hypothesis is not refuted, and the results are not statistically significant .
• If the p value is lower than the significance level, the results are interpreted as refuting the null hypothesis and reported as statistically significant .

Usually, the significance level is set to 0.05 or 5%. That means your results must have a 5% or lower chance of occurring under the null hypothesis to be considered statistically significant.

The significance level can be lowered for a more conservative test. That means an effect has to be larger to be considered statistically significant.

The significance level may also be set higher for significance testing in non-academic marketing or business contexts. This makes the study less rigorous and increases the probability of finding a statistically significant result.

As best practice, you should set a significance level before you begin your study. Otherwise, you can easily manipulate your results to match your research predictions.

It’s important to note that hypothesis testing can only show you whether or not to reject the null hypothesis in favor of the alternative hypothesis. It can never “prove” the null hypothesis, because the lack of a statistically significant effect doesn’t mean that absolutely no effect exists.

When reporting statistical significance, include relevant descriptive statistics about your data (e.g., means and standard deviations ) as well as the test statistic and p value.

There are various critiques of the concept of statistical significance and how it is used in research.

Researchers classify results as statistically significant or non-significant using a conventional threshold that lacks any theoretical or practical basis. This means that even a tiny 0.001 decrease in a p value can convert a research finding from statistically non-significant to significant with almost no real change in the effect.

On its own, statistical significance may also be misleading because it’s affected by sample size. In extremely large samples , you’re more likely to obtain statistically significant results, even if the effect is actually small or negligible in the real world. This means that small effects are often exaggerated if they meet the significance threshold, while interesting results are ignored when they fall short of meeting the threshold.

The strong emphasis on statistical significance has led to a serious publication bias and replication crisis in the social sciences and medicine over the last few decades. Results are usually only published in academic journals if they show statistically significant results—but statistically significant results often can’t be reproduced in high quality replication studies.

As a result, many scientists call for retiring statistical significance as a decision-making tool in favor of more nuanced approaches to interpreting results.

That’s why APA guidelines advise reporting not only p values but also  effect sizes and confidence intervals wherever possible to show the real world implications of a research outcome.

Aside from statistical significance, clinical significance and practical significance are also important research outcomes.

Practical significance shows you whether the research outcome is important enough to be meaningful in the real world. It’s indicated by the effect size of the study.

Clinical significance is relevant for intervention and treatment studies. A treatment is considered clinically significant when it tangibly or substantially improves the lives of patients.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test . Significance is usually denoted by a p -value , or probability value.

Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis .

When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant.

A p -value , or probability value, is a number describing how likely it is that your data would have occurred under the null hypothesis of your statistical test .

P -values are usually automatically calculated by the program you use to perform your statistical test. They can also be estimated using p -value tables for the relevant test statistic .

P -values are calculated from the null distribution of the test statistic. They tell you how often a test statistic is expected to occur under the null hypothesis of the statistical test, based on where it falls in the null distribution.

If the test statistic is far from the mean of the null distribution, then the p -value will be small, showing that the test statistic is not likely to have occurred under the null hypothesis.

No. The p -value only tells you how likely the data you have observed is to have occurred under the null hypothesis .

If the p -value is below your threshold of significance (typically p < 0.05), then you can reject the null hypothesis, but this does not necessarily mean that your alternative hypothesis is true.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). An Easy Introduction to Statistical Significance (With Examples). Scribbr. Retrieved July 22, 2024, from https://www.scribbr.com/statistics/statistical-significance/

Is this article helpful?

Pritha Bhandari

Other students also liked, understanding p values | definition and examples, what is effect size and why does it matter (examples), hypothesis testing | a step-by-step guide with easy examples, what is your plagiarism score.

If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser.

Statistics and probability

Course: statistics and probability   >   unit 6.

• Types of statistical studies
• Worked example identifying experiment
• Worked example identifying observational study
• Worked example identifying sample study
• Observational studies and experiments

Appropriate statistical study example

Want to join the conversation?

• Upvote Button navigates to signup page
• Downvote Button navigates to signup page
• Flag Button navigates to signup page

Video transcript