extravagant hypothesis definition and examples

Logical Fallacy of Extravagant Hypothesis / Complex Hypothesis Fallacy

Complex hypothesis is one of the many smokescreens that are used to cover the fact that the reasoning is based on one of the three fallacies of Agrippa's trilemma. Whenever a logical fallacy is committed, the fallacy has its roots in  . All human thought (without Divine revelation) is based on one of three unhappy possibilities. These three possibilities are infinite regression, circular reasoning, or axiomatic thinking. This problem is known as Agrippa's trilemma. Some have claimed that only logic and math can be known without Divine revelation; however, that is not true. Without Divine revelation, neither logic nor math can be known. Science is also limited to the pragmatic because of the weakness on human reasoning, which is known as Agrippa's trilemma.

The Logical Fallacy of Extravagant Hypothesis / Complex Hypothesis Fallacy occurs when an explanation that requires more assumptions is chosen over those hypotheses that require less assumptions.

It is very important to remember . In secularist thinking the only three options are to make any conclusions based on infinite regression, circular reasoning, or axiomatic thinking. Axiomatic thinking is a kind way of saying "making assumptions." Making assumptions is a kind way of saying "making things up" or "lying." For a person to put any weight on a hypotheses or a theory that involves assumptions is irrational because a chain of thought is only as strong as its weakest link. Made up stories and assumptions have no strength at all. On the other hand, for those who follow Christ, it is not necessary to have all reason destroyed by Agrippa's Trilemma, since you have another option. That option is Divine revelation. Going beyond what God reveals to you is unnecessary. For many things, it's OK to admit that you don't know. Don't make up stories and deceive yourself into thinking that fabrications are part of reality. Evidence that is brought from a secularist presupposition is always some form of hypotheses because of  .

This is difficult to analyze, since many, if not most, assumptions are never admitted to be assumptions. They are thought of as facts. They are parts of worldviews that seem more like reality than reality itself. Here, assumption is being defined as a proposition that cannot be absolutely proven to be true. The more complex the hypothesis, the more evidence is required, since more truth claims are being made. Evidence here is actual empirical evidence without interpretation. Interpretation turns into just-so stories very easily and includes assumptions. In science, theology, and politics, it is common to begin to have very complex structures of thought. In the process, problems are covered with just-so stories and assumptions. These just so-stories are ad hoc hypotheses or rescuing mechanisms to save the overall complex hypothesis. As various hypotheses are intertwined and become inter-dependent, they are sometimes presented as if one supports the other. Actually, they have become a single, highly complex, hypothesis. If part of this huge hypothesis falls, the entire thought structure is shaken.

Examples of the Logical Fallacy of Extravagant Hypothesis / Complex Hypothesis Fallacy

The Big-Bang-Billions-of-Years-No-Flood-Molecules-to-Man hypothesis is laden with many assumptions. It falls apart without them. Yet many of those who embrace this hypothesis and give it special privilege over other hypotheses are unaware of these assumptions. On the other hand, the Creation and the Flood are revealed by God as facts. There are no assumptions required so long as we don't go beyond what has been revealed. What has been revealed doesn't violate anything that can be observed or tested using scientific method.

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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• Null and Alternative Hypotheses | Definitions & Examples

Null and Alternative Hypotheses | Definitions & Examples

Published on 5 October 2022 by Shaun Turney . Revised on 6 December 2022.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis (H 0 ): There’s no effect in the population .
• Alternative hypothesis (H A ): There’s an effect in the population.

The effect is usually the effect of the independent variable on the dependent variable .

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, differences between null and alternative hypotheses, how to write null and alternative hypotheses, frequently asked questions about null and alternative hypotheses.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”, the null hypothesis (H 0 ) answers “No, there’s no effect in the population.” On the other hand, the alternative hypothesis (H A ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample.

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept. Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect”, “no difference”, or “no relationship”. When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis (H A ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect”, “a difference”, or “a relationship”. When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes > or <). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question
• They both make claims about the population
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis (H 0 ): Independent variable does not affect dependent variable .
• Alternative hypothesis (H A ): Independent variable affects dependent variable .

Test-specific

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Hypotheses (Non) Fingo

Toni vogel carey considers sir isaac newton’s most (in)famous remark..

For two centuries and more, Isaac Newton (1642-1727) was the very god of science, and commentators still hang on his every word, especially his most famous dictum, hypotheses non fingo . Besides, this saying makes for some intriguing, if not very flattering, stories about Newton himself.

The relevant passage occurs in the final General Scholium of Newton’s Principia (1687). (The book’s full title is Philosophiae Naturalis Principia Mathematica , or The Mathematical Principles of Natural Philosophy ). Here is the original English translation of 1729 by Francis Motte, which is still in use:

“Hitherto we have explained the phenomena of the heavens and of our sea by the power of gravity, but have not yet assigned the cause of this power … I have not been able to discover the cause of those properties of gravity from phenomena, and I frame no hypotheses [ hypotheses non fingo ]; for whatever is not deduced from the phenomena is to be called an hypothesis; and hypotheses, whether metaphysical or physical, whether of occult qualities or mechanical, have no place in experimental philosophy … To us it is enough that gravity does really exist, and acts according to the laws which we have explained, and abundantly serves to account for all the motions of the celestial bodies, and of our sea.”

Quite aside from its being in Latin, the Principia was such a difficult work that Newton, who apparently wanted it to be inscrutable to all but a few savants, advised the general reader to skip nearly all of Books I and II. (Unfortunately for the reader, this advice is proffered at the beginning of Book III.) So the average person, and most who were considerably above average, needed some sort of study notes to navigate the Principia . This made vulgarizations, or popularized glosses, indispensable, and in some cases very influential. Voltaire’s in 1738, although ‘lite’ on math, did much to convert the French from Cartesian to Newtonian science during the 1740s, which was very late, since the Principia had been taught at Scottish universities since the 1690s. The best vulgarization was by a Scottish protégé of Newton, Colin Maclaurin, who was appointed to the University of Edinburgh in 1725 on Newton’s recommendation, and who did more to introduce the Principia to England than Newton himself – who held the Lucasian chair at Cambridge from 1669 to 1702, and was President of the Royal Society of London from 1703 until his death in 1727!

Much, and I do mean much, has been made of the term fingo , and whether it should be taken to mean ‘frame’ or ‘make’ or ‘fashion’ or ‘feign’. Motte opted for ‘frame’, but ‘feign’ has become the favorite, because of its connotation of falseness, and Hume used the term this way more than once in his Treatise of Human Nature . But the constant reinterpretation of the phrase looks like little more than an attempt to construe hypotheses non fingo in a consistent way in the light of Newton’s lack of consistency even just in the General Scholium itself.

Let me explain. In contrast to the austerely mathematical treatise as a whole, this section contains a good deal of conversational and highly controversial commentary. Preceding the passage in question, Newton asserts not only that God is “infinite, omnipotent and omniscient,” but what’s more, that “to discourse of [God] from the appearances of things does certainly belong to Natural Philosophy.” As if that were not enough, following the passage in question is another “extravagant hypothesis,” to quote the Newton scholar I.B. Cohen (in Isis #53). This one concerns “a most subtle Spirit which pervades and lies hid in all gross bodies; by the force and action of which Spirit the particles of bodies mutually attract one another at near distances” – to wit, an all-pervading gravitational ‘aether’. You can already see why hypotheses non fingo is not exactly a slam-dunk.

Hypotheses, Phenomena, Rules

Three editions of the Principia appeared during Newton’s lifetime. However, serious study of the development of his thinking from the first to the third editions did not begin in earnest until the mid-twentieth century, when history of science became a recognized discipline. The evolution of the Principia from 1687 to 1726 is important, for one thing because the General Scholium did not appear until the second edition of 1713, nor therefore did hypotheses non fingo . And for another, only in the third edition of 1726 do we find all four of Newton’s ‘Rules of Reasoning’ ( Regulae philosophandi ). Briefly:

1. “We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances.” This is Newton’s principle of parsimony (which I wrote about in PN issue 81). His short version of Ockham’s Razor is, “More is in vain when less will serve.”

2. “To the same natural effects we must, as far as possible, assign the same causes.” He gives the examples of respiration in man and beasts, and the fall of stones in Europe and America. This principle is often stated the other way around, with a more predictive intent, i.e., from like causes we can infer like effects.

3. “Qualities … found to belong to all bodies within the reach of our experiments are to be esteemed the universal qualities of all bodies whatsoever.” This is Newton’s principle of induction.

4. “We are to look upon propositions collected by general induction from phaenomena as accurately or very nearly true … till such time as other phaenomena occur by which they may either be made more accurate, or liable to exceptions.” This rule renders scientific conclusions at root conditional, always subject to correction. The science scholar Alexandre Koyr é called it Newton’s “rule of prudence and of good sense.”

Book III is particularly revealing from our standpoint, because in 1726 it opens with the four ‘Rules’, whereas in 1687 it opened with a section labeled ‘Hypotheses’. Nine ‘hypotheses’ were scattered through the work in the first edition, of which only one survived as such in the second and third. But one hypothesis did survive as such, which means that hypotheses non fingo was evidently contradicted in the Principia itself. As for the other eight, six Hypotheses were renamed Phaenomena in later editions, and two were renamed Regulae philosophandi .

Up to a point, then, we can take Newton to be saying, I do frame hypotheses, I just call them rules . But Newton was surely right to call them rules, since they did for scientific method what Euclid’s axioms did for geometry and Aristotle’s laws (non-contradiction, etc) did for logic.

Newton vs. Hooke (It’s Not Even Close)

The story of Newton is first and foremost that of his science. But much of the rest is about his endless vituperative verbal battles, most notably with Robert Hooke (1635-1703) and Gottfried Leibniz (1646-1716). The war with Hooke in particular is relevant here, since it is intertwined with hypotheses non fingo . It began in 1672, when Newton’s first paper, on light and color, was presented to the Royal Society of London, and he was elected a Fellow. Hooke, who was already powerful in the Society, and possessed extraordinary knowledge and creativity in all matters scientific, had his own ideas about light (basically a wave theory), which led him to call Newton’s particle theory “only a hypothesis.”

Hooke’s problem was that while he initiated promising new ideas about almost everything, it was others who brought them to completion, and, to Hooke’s consternation, got credit for them. According to the Dictionary of National Biography he had a “jealous” and “peevish temper,” and was later to complain about the Principia that he “gave Newton the first hint” of the theory of gravitation. There was much truth in this; but Newton was the one who took it over the finish line.

Even before the Principia , however, things had already gone from bad to worse between the two men. For consider Newton’s second-most-famous saying: “If I have seen further it is by standing on the shoulders of giants.” This idea, which goes back to the first century, was hardly original with Newton. But most scholars have considered it a gracious expression of modesty on his part, which has done much to counter his reputation as haughty, dictatorial and even cruel. More than once, Dudley Shapere writes in his entry on Newton in the Encyclopedia of Philosophy , “he used the power of his reputation or office to crush others.”

Looks here, though, are deceiving. The shoulders-of-giants remark appeared in a letter in 1675/76, written to none other than Robert Hooke. And Hooke had been beset from his teenage years by a malady that made him look increasingly hunchbacked. Far from being a gracious compliment, then, or even just “an attempt to pacify Hooke,” as Shapere suggests, Newton was, if anything, upping his vindictiveness to new levels.

My Newton gossip comes mostly from a paper by Mordechai Feingold (‘Mathematicians and Naturalists: Sir Isaac Newton and the Royal Society’ in J.Z. Buchwald and I.B. Cohen, eds., Isaac Newton’s Natural Philosophy ), and from Frank Manuel, who wrote in his 1968 psychological biography A Portrait of Newton : “When the enemy was very powerful, as in the case of Robert Hooke, he tended to withdraw, to avoid, to hide away, to bide his time. But entrenched in office, he used virtually every means at his disposal to defeat an antagonist, and he required total submission, public humiliation, annihilation.”

The Royal Society

The phrase hypotheses non fingo may have originated with Henry Oldenburg, the first Secretary (executive director) of the Royal Society, who praised members in 1667 for “neither feigning nor formulating hypotheses of nature’s actions,” but rather seeking out “the thing itself.” In any case, Oldenburg’s importance to the Society from its inception in 1660 cannot be exaggerated. Almost instantly and singlehandedly, through his deft diplomacy in handling scientific correspondence and his editing of the Philosophical Transactions , the first important scientific journal, he made the Society the hub of scientific communication worldwide. T.H. Huxley would later declare that “if all the books in the world except the Philosophical Transactions were destroyed, it is safe to say that the vast intellectual progress of the last two centuries would be largely preserved.” Unfortunately, when Oldenburg died in 1677, Robert Hooke, who lacked Oldenburg’s people skills, not least in handling Newton, succeeded him as Secretary.

The war between Newton and Hooke, while intensely personal, played out within a larger split within the Royal Society’s mathematical and experimental factions. To be sure, this too had a lot to do with Newton, for the Society’s troubles began with its contested decision to publish the Principia : it was only through great persistence that the astronomer Edmund Halley (of comet fame) managed to shepherd the work to publication.

Before 1687 the experimental and mathematical wings of the Society had coexisted peacefully enough. And according to Feingold, until Newton’s Presidency commenced in 1703 the empiricists remained tolerant of the mathematicians, but not conversely: the mathematicians were already dismantling “the very cornerstone upon which the English empiricist scientific tradition stood: experiments and observations” (from ‘Mathematicians and Naturalists’). And for the quarter-century that Newton presided over the Society, his mathematical faction kept the empiricists completely subjugated.

The roots of the Royal Society were strongly empiricist, and it took a powerful force to counter this bent. Newton provided that force. But as soon as he died, the empiricists regained the upper hand in a bitter election which left the mathematicians less-than-gracious losers. The one person whose reputation miraculously emerged unscathed was Newton himself, who continued to be seen as “the greatest man that ever liv’d.” (quoted in Feingold op cit p.77) Alexander Pope penned the famous epitaph:

“Nature and Nature’s laws lay hid in night: God said, Let Newton be! and all was light.”

The praise here was for Newton’s physics and optics, the science of light. But at the Royal Society the skies, which had been bright until Newton showed up, soon turned dark and menacing.

Hypotheses Fingo

The factionalism within the Royal Society was noteworthy for its hostility; but it reflected a mathematical-empiricist split which characterized modern science from its inception. On one side, Galileo proclaimed that the book of nature is written in the language of geometry, without which we cannot understand a single word of it. On the other, Francis Bacon decreed that the way to do science is by generalization from observed instances, with “Mathematic and Logic… but the handmaids of Physic.” Nearly four centuries later, science is still divided along these methodological lines.

The elephant in the room in this story was Newton’s other great work, the Opticks , which appeared in 1704, just after he became President of the Society. Written and published in English, this book was far more accessible than the Principia , and it was read and studied by professionals and lay people alike. Moreover, as Cohen notes, the Opticks was where “eighteenth-century experimentalists [could] find Newton’s methods.” Yet judging by his stance at the Royal Society, you would think Newton had never written anything but the Principia . Even before becoming President he remarked haughtily that he “first proved his inventions by geometry and only made use of experiments to make them intelligible and to convince the vulgar.”

I don’t mean to suggest that Newton’s position on hypothesizing was any clearer in the Opticks than in the Principia . In its final Query 31 (which dates from the second edition of 1718), Newton talks about “making Experiments and Observations, and … drawing general Conclusions from them by Induction … For hypotheses are not to be regarded in experimental Philosophy.” That sounds very Baconian. But as Cohen notes, far from following hypotheses non fingo in the Opticks , Newton “let himself go, allowing his imagination full reign and by far exceeding the bounds of experimental evidence.”

From the outset, as I said, the Royal Society had a strong Baconian (empiricist) bias, as did the English-speaking world generally. But as time went on and facts kept piling up, it became increasingly obvious that they were failing to arrange themselves so as to yield satisfactory conclusions. Disillusionment inevitably set in, and by 1875 even the entry on Bacon in the Encyclopaedia Britannica was denouncing the “inductive formation of axioms by a gradually ascending scale” as one that “no science has ever followed, and by which no science could ever make progress. The true scientific procedure is instead that of hypothesis followed up and tested by verification.” Against that backdrop, it makes little sense to make hypotheses non fingo the formula to which “the whole Newtonian epistemology is reduced,” as Alexandre Koyré put it.

What emerges from Newton scholarship since 1950 is a position on hypothesizing that most today would find quite reasonable and middle-of-the-road, I think:

• According to Larry Laudan (in The Methodological Heritage of Newton , ed. R.E. Butts and J.W. Davis), in the early seventeenth century ‘hypothesis’ stood for “unproven postulates, axioms or first principles of any science” – an interpretation common since Aristotle and Euclid – and this was what Newton meant by it in the first edition of the Principia , where it carried no pejorative connotation. Cohen too says that Newton sometimes used ‘hypothesis’ and ‘axiom’ interchangeably, which helps to explain the transition from ‘hypotheses’ in the first edition to ‘rules’ in the third.

• Laudan also quotes a letter from Newton to Oldenburg:

“The best and safest method of philosophizing seems to be, first diligently to investigate the properties of things and establish them by experiment, and then seek hypotheses to explain them. For hypotheses ought to be fitted merely to explain the properties of things and not attempt to determine them.”

Newton was not opposed to hypotheses, then, so long as they were kept within the proper, explanatory, bounds.

• Cohen says that ‘hypothesis’ could also serve as a synonym for ‘suspicion’, a term used in the first paragraph of the Preface to the Principia ; or for ‘assumption’, as in Newton’s proviso, “on the assumption that the centre is (isn’t) stationery.” Newton himself used the term ‘conjecture’ in another comment that speaks directly to the hypotheses non fingo passage:

“It is not the Business of Experimental Philosophy to teach the Causes of things any further than they can be proved by Experiments. We are not to fill this Philosophy with Opinions which cannot be proved by Phenomena. In this Philosophy Hypotheses have no place, unless as Conjectures or Questions proposed to be examined by Experiments.”

• What seems to be the new consensus, expressed by Cohen and others, is that when Newton asserted hypotheses non fingo , he did not mean it as a sweeping methodological statement. Given his hypothesizing, that would make little sense. Rather, Newton was saying only that he would not frame/feign hypotheses about the cause of gravitation.

• My own hypothesis about hypotheses non fingo goes to Newton’s real passion, which was not physics but alchemy. He wrote something like a million words on the subject, far more than on any other. In 2007 the History of Science Society announced a new transcription of Newton’s manuscript Of Nature’s Obvious Laws & Processes in Vegetation , his youthful ‘theory of everything’, which shows that he “linked alchemy to his early theory of gravitation.” Newton must have been constantly on guard lest his patently hypothetical alchemical ideas might come to light; for that would have made him liable to censure, if not dismissal, from Cambridge University, to say nothing of his vulnerability to valid criticism from scientific peers. And we know how Newton reacted to criticism.

The question that emerges from all this, I think, is: What has all the fuss been about? Hypotheses non fingo looks like a cause célèbre created mostly by commentators trying, just as they did in his own day, to make Newton perfectly right. The “greatest man who ever liv’d” was buried nearly three centuries ago; yet apparently he still has the power to intimidate.

© Dr Toni Vogel Carey 2012

Toni Vogel Carey, a philosophy professor in a former life, is on the US board of advisors for Philosophy Now , and writes about the history of ideas.

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• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.