Inferential Statistics for Psychology Studies

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  • 0:53 Inferential Statistics
  • 1:15 Two Types of…
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Lesson Transcript
Instructor: Wind Goodfriend
Psychology is a science, which means that in order to understand people's thoughts and behaviors, a basic understanding of statistics is necessary. Most psychology studies use inferential statistics. This lesson covers basic types of inferential statistics, as well as how to decide whether a hypothesis was supported by the results.

Inferential Statistics

Imagine a teacher is interested in studying several aspects of her class, such as the personality of her students, whether boys are different from girls or whether different teaching styles lead to different results in her students. In order to understand any of these aspects of the children in her class, the teacher must understand some basic statistics so that she can quantify her understanding, or, in other words, put it into numerical form. In another lesson for Educational Psychology, you can learn about ideas such as the mean, median and mode to describe people, or how a standard bell curve works. This lesson focuses on a slightly different type of statistic, called inferential statistics.

Inferential statistics are ways of analyzing data that allow the researcher to make conclusions about whether a hypothesis was supported by the results. You can remember the term inferential because it comes from the word 'inference,' meaning 'to draw a conclusion from clues in the environment.' How do inferential statistics work?

Two Types of Inferential Statistics

To make things easier, let's think about an example from a classroom. Imagine a teacher suspects that the boys in her class are more extroverted - or more talkative, energetic and social - than the girls in her class. The teacher's guess about the difference between boys and girls is what we call a hypothesis. In psychology, a hypothesis is an educated guess about a trend, group difference or association believed to exist. Her hypothesis is that boys are more extroverted than girls. How would she test this hypothesis? The teacher would probably do something to measure extroversion, such as give the students a personality survey to complete, or simply observe them and keep track of extroverted behaviors. Either way, she can measure the level of extroversion in every boy and every girl. Then, she can compare the scores across the two groups.

The first type of inferential statistic we need to discuss is called a t-test. A t-test is used to compare the average scores between two different groups in a study to see if the groups are different from each other. In our example, the teacher would use a t-test to compare the average level of extroversion in the group of boys versus the group of girls. T-tests are very common in psychology because they can be used to compare any two groups in an experiment. If you do an experiment where you ask some people to eat healthy food and some people to eat unhealthy food, such as candy, you could then test them on some variable, such as whether they get a stomachache. A t-test would again be used here, because you are comparing the two different groups. You can use t-tests to compare two groups that occur naturally, such as boys versus girls, or you can compare two groups that you have created in an experiment.

So a t-test compares two groups. You can remember the term t-test by pretending that the letter 't' stands for the word 'two,' meaning the two groups you are comparing. But what if you want to compare more than two groups? Imagine that the teacher thinks that as children age, they become more extroverted. Now she might give personality tests to children in each grade level in the school, such as all the way from kindergarten to sixth grade. How could she compare all of these different groups, now that we have more than two?

The second basic type of inferential statistics is called an analysis of variance. Researchers usually use the nickname ANOVA for this test. An analysis of variance is a test that compares the average scores between three or more different groups in a study to see if the groups are different from each other. In other words, an ANOVA is exactly the same as a t-test, but it can analyze multiple groups at once. The difference is simply how the equation works to analyze the groups, which you can learn more about in a statistics class if you're interested. For now, all you need to know is that the ANOVA compares multiple groups, while a t-test can only compare two groups.

Let's go through one more example of when you might use each test. Imagine a teacher believes that different teaching styles result in different scores when children take a test over the material. He might try lecturing for one group of students, versus worksheets with a second group of students. He then gives everyone the same test, and wants to compare the results. If he only had these two groups, he would use a t-test to compare the scores. However, now let's say that he wanted to add a third teaching style, which was having the students learn the material on their own and then teach it to each other. If he now wants to compare all three teaching styles to each other, he would use an analysis of variance, or an ANOVA test.

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