Sample size for independent samples t-test
Effect size is the difference in means between two groups on the outcome
A good rule of thumb is to overestimate the variance of the effect size when calculating the sample size for an independent samples t-test. Researchers do this because it forces them to have to collect more observations of the outcome, which in turn leads to more precise and accurate measures of effect.
For example, let's say that researchers find quality evidence that people in the treatment group sleep an average of 7.5 hours a night with a standard deviation of 1.5 and people in the control group sleep an average of 6.7 hours a night with a standard deviation of 2.1. There is an evidenced-based measure of effect of 0.8 hours (7.5 - 6.7 = 0.8). Researchers could enter these values into G*Power and know exactly how many observations of the outcome they would need to collect in order to detect that 0.8 hour treatment effect.
The steps for calculating sample size for an independent samples t-test in G*Power
2. Under the Test family drop-down menu, select t tests.
3. Under the Statistical test drop-down menu, select Means: Difference between two independent means (two groups).
4. Under the Type of power analysis drop-down menu, select A priori: Compute required sample size - given alpha, power, and effect size.
5.If there is a directional hypothesis, under the Tail(s) drop-down menu, select One.
6. If there is a non-directional hypothesis, under the Tail(s) drop-down menu, select Two.
7. Click the Determine button.
8. Enter the mean for the treatment group into the Mean group 1 box. Example: "7.5"
9. Enter the mean for the control group into the Mean group 2 box. Example: "6.7"
10. Enter the standard deviation associated with the mean of the treatment group into the SD insert group 1 box. Example: "1.5"
11. Enter the standard deviation associated with the mean of the control group into the SD insert group2 box. Example: "2.1"
12. Click Calculate.
13. Click Calculate and transfer to main window.
14. Leave the alpha value at 0.05, unless researchers want to change the alpha value according to the current empirical or clinical context.
15. Enter ".80" into the Power (1-beta err prob) box, unless researchers want to change the power according to the current empirical or clinical context.
16. If researchers have exactly equally sized groups, then leave the Allocation ratio N2/N1 value at "1." If researchers have unequally sized groups, then divide the sample size of the treatment group by the sample size of the control group and enter that value into the box.
17. Click Calculate.
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