In a study, you design a hypothesis, which is what you think the outcome of your study will be. When you create a hypothesis, you are creating a null hypothesis and an alternate hypothesis.

• Null hypothesis: The independent variable has no effect on the dependent variable, meaning that nothing will change during the study.
• Alternate hypothesis: The independent variable has an effect on the dependent variable, meaning that you expect to see change during the study.

For example, you’re designing a study on the effects of a therapeutic intervention on participants experiencing anxiety, with the intervention as your independent variable and the level of anxiety as the dependent variable. Your null hypothesis would say that the intervention has no effect on the participant’s anxiety level. Your alternative hypothesis would say that the intervention has an effect on the anxiety level.

EFFECT SIZE AND POWER

Effect size deals with the strength of the relationship between your two variables and what the magnitude of that effect is on your dependent variable. Let’s return to the same example as above. Your participant experiences anxiety 10 out of 24 hours in a day. The participant went through the intervention, and the hours reduced from 10 to 1 hour per day. This would indicate a strong effect size.

Power relates to the degree of change in the dependent variable just like effect size. However, statistical power also measures your likeliness of rejecting the null hypothesis. If your statistical power is high, you are seeing change in the dependent variable. Therefore, it is very likely that you will reject the null hypothesis, which states that there would be no change. Since the participant’s level of anxiety reduced from 10 hours to 1 hour, you can determine that the statistical power is high.

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