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Psychology 102: Educational Psychology10 chapters | 123 lessons | 9 flashcard sets

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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.

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?

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.

Now you know about two types of inferential statistics, the t-test and the ANOVA. But how do you know if your hypothesis was supported by the data? Let's go back to the example of the hypothesis that boys are more extroverted than girls. Imagine the teacher scores the surveys and finds that on a scale of 0-100, the average score for boys is 51, while the average for girls is 49. There's only a difference of two points here. Is that enough for him to decide that boys are more extroverted? What if there's a particularly energetic boy in the class, and a particularly shy girl, and those two students are primarily responsible for the difference in means? What if the scores were 5 points apart from each other? Would 10 points be enough?

Psychologists have decided that we need a way to decide whether any group differences are large enough to make a safe conclusion that the two groups really are different and that the results aren't simply due to chance or to participants who contribute extreme scores that affect the averages. How do we do this? The answer is something called a **p-value**. Whenever we do any statistical test in psychology, including a correlation, a t-test or an ANOVA, the calculation produces a second number, which is the p-value. The p-value tells you the likelihood that the results in the study would have happened simply by random chance.

The number you get with a p-value will always range between 0.00 (which means a zero percent chance that the results happened randomly) and 1.00, which indicates a 100% chance that the results were random. In order for us to make a solid conclusion, we want that number to be as low as possible, or as close to zero. Let's go back to the example. If a teacher found that the average boy score was 51, and the average girl score was 49, those numbers are very close to each other. So it's possible that a random boy or girl in the class affected the averages and that if he had used a different group of boys and girls, the numbers would have been different. Because of the high amount of uncertainty in these results, our p-value would probably be very high, such as around 90%. This means our p-value would be 0.90.

However, if the scores were very different from each other, we could be more confident. If the average boy score was around 85, and the average girl score was around 15, then we can be very confident that most boys are more extroverted than most girls. So, the possibility that these scores occurred by chance would be very low - maybe around 4%. That means our p-value would be 0.04.

How sure do we need to be before we can decide if a hypothesis is supported? As a general rule, most psychologists have decided that we should only accept a maximum of a 5% chance that the scores happened at random - in other words, we should be 95% sure that our group differences really aren't due to chance. That means that we want a p-value of between 0% and 5% chance, which would look like a number between 0.00 and 0.05. Any p-value of 0.05 or less means that we can be very sure that our results are valid, and not simply due to chance factors in the study.

**Inferential statistics** are what psychologists use to decide whether hypotheses are supported or not by the results of any study. **T-tests** compare scores in two different groups, whereas **analysis of variance**, or **ANOVA**, tests compare three or more different groups.

In order to make sure our results didn't happen due to random chance, we look for a **p-value** somewhere between 0.00 and 0.05, which tells us that there's less than a 5% chance the results were random. These concepts can help anyone set up a basic psychology study correctly.

After watching this lesson, you should be able to:

- Define inferential statistics
- Differentiate between a t-test and an analysis of variance (ANOVA)
- Explain what a p-value is and how it is used in statistics

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Psychology 102: Educational Psychology10 chapters | 123 lessons | 9 flashcard sets

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