What is the recommended sample size for quantitative methods?

In quantitative research, a recommended sample size that is greater than 30 is significant due to several reasons related to statistical power and the central limit theorem. The central limit theorem states that with a sufficient sample size, the distribution of the sample means will approach a normal distribution, regardless of the shape of the population distribution. This is particularly important when conducting hypothesis testing or making inferences about a population based on sample data.

Having a sample size greater than 30 generally allows researchers to obtain more reliable estimates of population parameters and increases the robustness of statistical analyses. It reduces the margin of error, enhances the precision of estimates, and allows for more sophisticated statistical tests to be employed. Additionally, larger sample sizes tend to provide more variability in data, which is crucial for making valid inferences.

When considering smaller sample sizes, such as those less than 30, researchers may experience challenges in attaining reliable results and achieving statistical significance, especially when dealing with diverse populations or conducting complex analyses. Therefore, selecting a sample size greater than 30 is a common guideline in quantitative research to ensure the validity and reliability of the findings.