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Correct Method of Calculating Minimum Sample Size in Quantitative Research: Statistical Power Analysis vs. Slovin's Formula

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Suppose a lot of lanzones have been placed in a large tray. If you want to know if they are sweet or not, do you need to count all the lanzones (population)? Not really, right? You just need to taste a sample. The total count of the lanzones (population) is not needed.

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It's the same in quantitative research. If you want to test whether there is a relationship between X and Y, or if there is a significant difference between the means of groups A and B, or if there is a significant effect of X on Y, you don't need the population size.

As long as the research uses statistical tests like T-test, ANOVA, Chi-square test, Pearson correlation, Regression, SEM, among others, the population size is not a requirement. It is incorrect to use Slovin's formula to calculate the sample size when the research employs these statistical models. Refer to the article titled "On the Misuse of Slovin's Formula"

The correct way to calculate the minimum sample size for your study is through Statistical Power Analysis. Therefore, it is essential to learn Statistical Power Analysis. Do not insist on using Slovin's Formula.

If you are using PLS-SEM for your study and want to know the concepts and guidelines on determining the minimum sample size, please visit this guide on PLS-SEM sample size requirements.

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