Effect Size in Hypothesis Testing: Definition & Interpretation

Effect Size in Hypothesis Testing: Definition & Interpretation
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  • 0:05 Hypothesis Testing
  • 0:43 Effect Size Definition
  • 1:04 Interpreting Effect Size
  • 1:22 Using Effect Size
  • 2:11 Lesson Summary
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Lesson Transcript
Instructor: Yuanxin (Amy) Yang Alcocer

Amy has a master's degree in secondary education and has taught math at a public charter high school.

Watch this video lesson to learn what effect size is when used in hypothesis testing. Also learn what significance it has in your testing. Learn how your data affects the effect size.

Hypothesis Testing

The formal procedure statisticians follow to determine whether a certain hypothesis is valid or not is referred to as hypothesis testing. By using hypothesis testing, statisticians can validate statements such as, 'This washer only needs one gallon of water to wash a large load of clothes.'

Hypothesis testing is a 4-step process.

Step 1: Write the hypothesis.
Step 2: Create an analysis plan.
Step 3: Analyze the data.
Step 4: Interpret the results.

In this lesson, we'll talk about the effect size in hypothesis testing and learn what it tells us about our data.

Effect Size Definition

We define effect size as the objective and standardized measure of the size of a particular effect. We can think about our effect size as the importance of a certain effect. The larger the effect size, the more important the effect. The more important the effect, the more easily it can be seen by just looking.

Interpreting Effect Size

How can we interpret this? For example, if our effect is the growing of beards by men, we can say that a large effect size will mean that there are more men who grow beards. A small effect size means there are few men who grow beards. If our effect size is 0, then there are no men who grow beards.

Using Effect Size

Because the effect size is an objective and standardized way of measuring effect, we can use it compare different hypothesis tests to each other. We can compare the effect sizes of the different hypothesis tests to see which hypothesis test gives us the greatest effect size or the least effect size.

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