## About The What Researchers Mean By Series

This research term explanation first appeared in a regular column called What researchers mean by that ran in the Institute for Work & Healths newsletter *At Work* for over 10 years . The column covered over 35 common research terms used in the health and social sciences. The complete collection of defined terms is available online or in a guide that can be downloaded from the website.

It’s easy for non-scientists to misunderstand the term significant when they come across it in an article. In everyday English, the word means “important.” But when researchers say the findings of a study were “statistically significant,” they do not necessarily mean the findings are important.

Statistical significance refers to whether any differences observed between groups being studied are “real” or whether they are simply due to chance. These can be groups of workers who took part in a workplace health and safety intervention or groups of patients participating in a clinical trial.

Let’s consider a study evaluating a new weight loss drug. Group A received the drug and lost an average of four kilograms in seven weeks. Group B didn’t receive the drug but still lost an average of one kg over the same period. Did the drug produce this three-kg difference in weight loss? Or could it be that Group A lost more weight simply by chance?

## Math Classes For Psychology Majors

Many prospective psychology students assume that their chosen major will require very little math. After all, psychology is the science of the mind and behavior, so what does math have to do with it?

Quite a bit, actually. Math classes, and statistics in particular, are an important part of any psychology program. You will need to take math classes that fulfill your school’s general education requirements as well as additional statistics requirements to fulfill your psychology program’s core requirements.

In most cases, you will have to take at least two math classes, but in other cases, it might end up being between three and five. Check your school’s graduation requirements as well as your psychology program’s core requirements for more information.

## A Brief History Of Misinterpretations

For as long as it has been used, NHST has been criticized for being defined or interpreted incorrectly. Bakan stated The psychological literature is filled with misinterpretations of the nature of the test of significance . At the time he even caveated his article noting that What will be said in this paper is hardly original . Giving credence to Bakans observation that he was not saying anything new, Rozeboom critiqued the application and misinterpretation of NHST by psychologists noting that NHST had attained the status of a religious conviction . In the same year, Nunnally referred to NHST as misused and misconceived .

After a decade or so passed since Bakans paper, Carver noted that not much had changed with respect to the application and interpretation of NHST. He then outlined what he referred to as fantasies about statistical significance. He identified three fantasies, odds-against chance fantasy, replicability fantasy, and valid research hypothesis fantasy, which categorized incorrect inferences that were drawn from significance tests.

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## The Real Problem Isnt With Statistical Significance; Its With The Culture Of Science

The authors of the latest *Nature *commentary arent calling for the end of p-values. Theyd still like scientists to report them where appropriate, but not necessarily label them significant or not.

Theres likely to be argument around this strategy. Some might think its useful to have simple rules of thumb, or thresholds, to evaluate science. And we still need to have phrases in our language to describe scientific results. Erasing statistical significance might just confuse things.

In any case, changing the definition of statistical significance, or nixing it entirely, doesnt address the real problem. And the real problem is the culture of science.

In 2016, Vox sent out a survey to more than 200 scientists asking, If you could change one thing about how science works today, what would it be and why? One of the clear themes in the responses: The institutions of science need to get better at rewarding failure.

One young scientist told us, I feel torn between asking questions that I know will lead to statistical significance and asking questions that matter.

The biggest problem in science isnt statistical significance; its the culture. She felt torn because young scientists need publications to get jobs. Under the status quo, in order to get publications, you need statistically significant results. Statistical significance alone didnt lead to the replication crisis. The institutions of science incentivized the behaviors that allowed it to fester.

## Finding Significance In Data

Even when we find patterns in data, often there is still uncertainty in various aspects of the data. For example, there may be potential for measurement errors . Or we may only have a snapshot of observations from a more long-term process or only a small subset of individuals from the **population** of interest. In such cases, how can we determine whether patterns we see in our small set of data is convincing evidence of a systematic phenomenon in the larger process or population? Lets take a look at another example.

**Example 2**: In a study reported in the November 2007 issue of *Nature*, researchers investigated whether pre-verbal infants take into account an individuals actions toward others in evaluating that individual as appealing or aversive . In one component of the study, 10-month-old infants were shown a climber character that could not make it up a hill in two tries. Then the infants were shown two scenarios for the climbers next try, one where the climber was pushed to the top of the hill by another character , and one where the climber was pushed back down the hill by another character . The infant was alternately shown these two scenarios several times. Then the infant was presented with two pieces of wood and asked to pick one to play with.

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## What Is Statistical Significance Used For

Statistical significance is important in a variety of fields**any time you need to test whether something is effective, statistical significance plays a role.**

This can be very simple, like determining whether the dice produced for a tabletop role-playing game are well-balanced, or it can be very complex, like determining whether a new medicine that sometimes causes an unpleasant side effect is still worth releasing.

Statistical significance is also frequently used in business to determine whether one thing is more effective than another. This is called A/B testingtwo variants, one A and one B, are tested to see which is more successful.

In school, you’re most likely to learn about statistical significance in a science or statistics context, but it can be applied in a great number of fields. Any time you need to determine whether something is demonstrably true or just up to chance, you can use statistical significance!

## Statistical Significance Versus Practical Significance

Table 13.1 illustrates another extremely important point. A statistically significant result is not necessarily a strong one. Even a very weak result can be statistically significant if it is based on a large enough sample. This is closely related to Janet Shibley Hydes argument about sex differences . The differences between women and men in mathematical problem solving and leadership ability are statistically significant. But the word;*significant*;can cause people to interpret these differences as strong and importantperhaps even important enough to influence the college courses they take or even who they vote for. As we have seen, however, these statistically significant differences are actually quite weakperhaps even trivial.

Key Takeaways

Exercises

*r*;= .78 based on a sample size of 137.

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## Why Are Statistics Necessary In Psychology

A lot of psychology students are surprised to realize that statistics courses are required for graduation in their chosen major. Yes, statistics courses are a major part of virtually all psychology programs. You will also encounter the subject in many of your other classes, particularly those that involve experimental design or research methods.

To succeed in psychology, you not only need to be able to pass a statistics class. You need to be able to understand statistics, too.

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Whats remarkable is not only that mid-20th century psychology textbook writers and publishers fabricated significance testing out of a mishmash of conflicting statistical techniques . Its also that their weird creation was embraced by many other disciplines over the next few decades. It didnt matter that eminent statisticians and psychologists panned significance testing from the start. The concocted calculation proved highly popular in social sciences, biomedical and epidemiological research, neuroscience and biological anthropology.

A human hunger for certainty fueled that academic movement. Lacking unifying theories to frame testable predictions, scientists studying the mind and other human-related topics rallied around a statistical routine. Repeating the procedure provided a false but comforting sense of having tapped into the truth. Known formally as null hypothesis significance testing, the practice assumes a null hypothesis and then rejects that hypothesis if the P value for observed data came out to less than 5 percent .

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## Role In Statistical Hypothesis Testing

Statistical hypothesis testingNull hypothesisAlternative hypothesisp-valueType I and type II errorstwo-tailed testsampling distribution

Statistical significance plays a pivotal role in statistical hypothesis testing. It is used to determine whether the null hypothesis should be rejected or retained. The null hypothesis is the default assumption that nothing happened or changed. For the null hypothesis to be rejected, an observed result has to be statistically significant, i.e. the observed *p*-value is less than the pre-specified significance level .

To determine whether a result is statistically significant, a researcher calculates a *p*-value, which is the probability of observing an effect of the same magnitude or more extreme given that the null hypothesis is true. The null hypothesis is rejected if the *p*-value is less than a predetermined level, is also called the *significance level*, and is the probability of rejecting the null hypothesis given that it is true . It is usually set at or below 5%.

## How Is Statistical Significance Defined In Research

Theworld today is drowning in data.

That may sound like hyperbole but consider this. In 2018, humans around the world produced more than 2.5 quintillion bytes of dataeach day. According to some estimates, every minute people conduct almost 4.5 million Google searches, post 511,200 tweets, watch 4.5 million YouTube videos, swipe 1.4 million times on Tinder, and order 8,683 meals from GrubHub. These numbersand the worlds total dataare expected to continue growing exponentially in the coming years.

For behavioral researchers and businesses, this data represents a valuable opportunity. However, using data to learn about human behavior or make decisions about consumer behavior often requires an understanding of statistics and statistical significance.

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## How To Calculate Statistical Significance

**Calculating statistical significance is complexmost people use calculators rather than try to solve equations by hand.**Z-test calculators and t-test calculators are two ways you can drastically slim down the amount of work you have to do.

However, learning how to calculate statistical significance by hand is a great way to ensure you really understand how each piece works. Let’s go through the process step by step!

## Test Statistics And P Values

Every statistical test produces:

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

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 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.

To interpret your results, you will compare your *p *value to a predetermined significance level.

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## Practical Significance Vs Statistical Significance

To demonstrate the difference between practicaland statistical significance, imagine youre a candidate for political office.Maybe you have decided to run for local or state-wide office, or, if yourefeeling bold, imagine youre running for President.

During your campaign, your team comes toyou with data on messages intended to mobilize voters. These messages have beenmarket tested and now you and your team must decide which ones to adopt.

If you go with Message A, 41% ofregistered voters say they are likely to turn out at the polls and cast aballot. If you go with Message B, this number drops to 37%. As a candidate,should you care whether this difference is statistically significant at a *p* value below .05?

The answer is of course not. What youlikely care about more than statistical significance is **practicalsignificance**the likelihood that the difference between groups is largeenough to be meaningful in real life. ;

You should ensure there is some rigor behind the difference in messages before you spend money on a marketing campaign, but when elections are sometimes decided by as little as one vote you should adopt the message that brings more people out to vote. Within business and industry, the practical significance of a research finding is often equally if not more important than the statistical significance. In addition, when findings have large practical significance, they are almost always statistically significant too.

## What Is Statistical Significance How Is It Calculated

If you’ve ever read a wild headline like, “Study Shows Chewing Rocks Prevents Cancer,” you’ve probably wondered how that could be possible. **If you look closer at this type of article you may find that the sample size for the study was a mere handful of people.** If one person in a group of five chewed rocks and didn’t get cancer, does that mean chewing rocks prevented cancer?

Definitely not. The study for such a conclusion doesn’t have statistical significancethough the study was performed, its conclusions don’t really mean anything because the sample size was small.

So what is statistical significance, and how do you calculate it? **In this article, we’ll cover what it is, when it’s used, and go step-by-step through the process of determining if an experiment is statistically significant on your own.**

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## Task : The Influence Of Body Movement On Information Processing Speed

Previous studies have shown that body movements can influence cognitive processes. For instance, it has been shown that movements like bending an arm for pulling an object nearer go along with diminished cognitive control. Likewise, participants showed more cognitive control during movements pushing away from the body. In this study, the influence of movement of the complete body on speed of information processing was investigated.

The hypothesis was that stepping back leads to more cognitive control, i.e., more capacity. There were two conditions in this study: In the first condition participants were taking four steps forwards, and in the second condition participants were taking four steps backwards. Directly afterwards they worked on a test capturing attention in which their responses were measured in milliseconds. The mean reaction time of the stepping forward-condition was compared to the mean reaction time of the stepping backward-condition.

*Researchers found a statistically significant effect in this study.*

## Overuse In Some Journals

Starting in the 2010s, some journals began questioning whether significance testing, and particularly using a threshold of =5%, was being relied on too heavily as the primary measure of validity of a hypothesis. Some journals encouraged authors to do more detailed analysis than just a statistical significance test. In social psychology, the journal *Basic and Applied Social Psychology* banned the use of significance testing altogether from papers it published, requiring authors to use other measures to evaluate hypotheses and impact.

Other editors, commenting on this ban have noted: “Banning the reporting of *p*-values, as Basic and Applied Social Psychology recently did, is not going to solve the problem because it is merely treating a symptom of the problem. There is nothing wrong with hypothesis testing and *p*-values per se as long as authors, reviewers, and action editors use them correctly.” Some statisticians prefer to use alternative measures of evidence, such as likelihood ratios or Bayes factors. Using Bayesian statistics can avoid confidence levels, but also requires making additional assumptions, and may not necessarily improve practice regarding statistical testing.

The widespread abuse of statistical significance represents an important topic of research in metascience.

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## Significance Thresholds In Specific Fields

In specific fields such as particle physics and manufacturing, statistical significance is often expressed in multiples of the standard deviation or sigma of a normal distribution, with significance thresholds set at a much stricter level . For instance, the certainty of the Higgs boson particle’s existence was based on the 5 criterion, which corresponds to a *p*-value of about 1 in 3.5 million.

In other fields of scientific research such as genome-wide association studies, significance levels as low as 5×108 are not uncommonas the number of tests performed is extremely large.