Interpreting a Non-Significant Outcome

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  • 0:02 Statistical Analysis
  • 2:12 Non-Significance
  • 3:31 Interpretation
  • 5:30 Lesson Summary
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Lesson Transcript
Instructor: Natalie Boyd

Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.

Research can take a lot of time for the person conducting it. So what happens when the statistics show that the results are not significant? In this lesson, we'll look at what a non-significant outcome means and what it doesn't mean.

Statistical Analysis

Sophie is a psychologist who believes that people like other people who mirror their body language. In other words, she believes that if you are leaning to your right, you'll be more likely to have a positive impression of someone who is leaning to their left when they talk to you.

To test her theory, Sophie has subjects talk to a confederate, or a researcher pretending to be a subject in a study. In the experimental condition, the confederate mirrors the body language of the actual subject as they have a conversation about something mundane, like the weather. In the control condition, the confederate just stands still and doesn't mirror the body language of the subject.

Afterward, Sophie gives the subjects a survey and asks them to rate, from 1 to 5, how much they liked the person they talked with. A 5 means that they thought that person was awesome, and a 1 means that they didn't like the confederate at all. If Sophie is right, the people in the experimental condition, where the confederate mirrored the subject, will like the confederate more than the people in the control condition, where the confederate didn't mirror their body language.

When Sophie averages the scores out, she finds that the people in the experimental condition rated their confederates as a 4.3, while people in the control condition rated their confederates as a 3.6. That sure sounds like Sophie is right, but is her result because of the experiment, or is it due to chance?

To test if the result is significant (that is, likely because of the experiment and not due to chance), Sophie has to run statistical analysis on her means. There are many different types of statistical analysis: t-tests, ANOVA, regression, chi-square. All are used for different types of research questions and different types of data. For our purposes, let's just say that Sophie chooses an appropriate statistical analysis, and she gets a significant result. That is, the difference in the average rating of the groups is most likely because of what the confederate did, not due to chance.


But what happens if Sophie's statistics were not significant? What then? Non-significance in statistics means that the null hypothesis cannot be rejected. In laymen's terms, this usually means that we do not have statistical evidence that the difference in groups is not due to chance.

Notice that there are two negatives in that sentence: we do not have evidence that the difference is not due to chance. Anyone who's had a strong English teacher can tell you that, in English, two negatives equal a positive. But in statistics, it's a little different. We can't say that the difference is due to chance, only that we don't have evidence that it's not due to chance.

This might seem like a minor difference, so let's go back to Sophie's study for a moment to see what this means in practice. If Sophie gets a non-significant result on her statistical analysis, it means that she can't rule out the possibility that the difference in the two groups is caused by chance. However, it does not mean that the difference is caused by chance. It might still be caused by the mirroring of body language, or it might be caused by something else. All she knows for certain is that it might be caused by chance.

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