Ensuring an appropriate sample size prior to collecting data is critical. Sample size calculations depend on three factors:-error, -error, and effect size. This is to prevent Type 1 errors, Type 2 errors, and to ensure the power of the study is appropriate, respectively. Too small a sample will not be statistically significant or generalizable and too large a study could negatively impact too many people unnecessarily, among other concerns (Nayak, 2010).

For example, Button et al. (2013) argued that too small of sample sizes undermine research in neuroscience, as it has less chance of detecting an effect. This leads to practitioners taking action, such as recommending treatments, based on false research outcomes (Button et al., 2013).Sample size errors can occur for several reasons, including the researcher basing the size purely on convenience rather than considering the purpose of the study and its design. Because the sample size must be based on the study design and research question, and determines the amount of data to be collected, it only makes sense to determine the sample size prior to starting data collection. This is particularly true in quantitative experimental and quasi-experimental studies, when intervention and control groups are being examined. Introducing new participants could throw off results.

Do you agree or disagree and why? please explain