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As a reviewer, I would start by stating that statistical significance involves a statistics critical value. It helps determine whether the null hypothesis needs to be rejected or fails to get rejected. On the other hand, meaningfulness implies a statistics efficiency and applicability to the real world (Laber & Shedden, 2017). Therefore, a significant and meaningful statistic will be applicable to predict different hypotheses and determine their stand without bias.
The statement provided in the footnotes denotes that the exploratory research tool was utilized in completing a qualitative research study. It is important to note that exploratory research mainly helps in generating formal hypotheses. Additionally, a formal hypothesis stands for any idea suggested as a probable explanation for a specific phenomenon or occurrence, awaiting approval as correct or false. In addition, the traditional level of significance uses 0.05 or 0.01, which is 1%, while the choice may be subjective (William, 2020). A lower significance level will have a lower error rate. When the significance level is low, the statistical analysis gets more conservative. For it to be significant, the data being used must diverge more from the null hypothesis.
The traditional significance level provides a basis to reject the null hypothesis in a statistical test when it is true. It should be chosen by considering factors like sample size, power of the test, and implications caused by type I and II errors. Nevertheless, a null hypothesis will refute research hypotheses and contradict them, stating that there lacks any difference between population means and a designated probability value (Frankfort-Nachmias et al., 2020). The probability value is usually stringent and stands at <0.05. In this case, reducing the ultimate strictness by relaxing the probability value to <0.10 will make hypothesis predictability weak and biased.
References
Frankfort-Nachmias, C., Leon-Guerrero, A. Y., & Davis, G. (2020). Social statistics for a diverse society. Sage Publications.
Laber, E. B., & Shedden, K. (2017). Statistical significance and the Dichotomization of evidence: The relevance of the ASA statement on statistical significance and P-values for statisticians. Journal of the American Statistical Association, 112(519), 902-904.
William E. Wagner, I. (2020). Using IBM® SPSS® statistics for research methods and social science statistics. SAGE Publications.
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