When it comes to calculating the In general, the sample needs to be big enough to guarantee the generalizability of results, and small enough to answer the research questions via the research sources available (Peat, 2011). However, calculating the sample size is always prone to errors, as explained above. In fact, calculating the sample size is a subjective process. For example, in large samples, some outcomes may appear statistically significant, while in clinical settings, they are unimportant. On the other hand, small samples may reveal some important clinical differences, which due to the small sample size do not show any statistical significance.

Experts need to be familiar with such issues to avoid them. In fact, the problems presented above are known as oversized and undersized studies and clinical trials. What’s more, when the study is oversized, type I error may occur. Type I error is defined as the wrong rejection of a true null hypothesis. To be more precise, this happens when the null hypothesis is true, but researchers reject it and accept the alternate one, which is the hypothesis explored by their team. Thus, oversized studies may waste resources and become unethical due to any excessive enrollment of subjects. On the other side, when the study is undersized, both type I and II errors may occur. Type II error is defined as the inability to reject a false null hypothesis. In other words, researchers may fail to reject the null hypothesis, which is untrue when compared to the alternate hypothesis. In fact, a small sample will often lead to inadequate statistical analyses. Undersized studies may also become unethical – simply because they won’t be able to fulfill the research goals of the study (Peat, 2011). Note that when sampling errors occur, it’s better to terminate a study rather than waste resources or mislead subjects.