Two ways in which practical statistics outweighs statistical significance?
Practical statistics encompasses the use of statistical methods to understand and interpret data in a meaningful and applicable way. While statistical significance is an important concept in hypothesis testing, there are situations where practical statistics can offer valuable insights that outweigh the sole reliance on statistical significance. Here are two ways in which practical statistics can outweigh statistical significance:
1. Effect size: Statistical significance focuses on determining whether an observed difference between groups is due to chance or a real effect. However, statistical significance alone does not provide information about the magnitude or practical importance of the effect. In contrast, practical statistics often considers effect size, which quantifies the strength of the relationship or the magnitude of the observed effect. By assessing effect size, practical statistics allows for a better understanding of the practical significance of the findings, which can be more meaningful when making decisions or performing practical applications.
To calculate effect size, various measures can be used depending on the statistical test or analysis you are conducting. For example, in t-tests or ANOVA, Cohen's d, eta-squared, or partial eta-squared can be used as effect size measures.
2. Context and relevance: Statistical significance is determined by a pre-defined level of significance (often denoted as alpha), which helps to control for Type I errors. However, adhering solely to statistical significance might lead to overlooking important contextual considerations and practical implications. Practical statistics takes into account the broader context, including the subject matter, practical significance, and relevance of the findings to real-world situations.
To address this, it is important to apply critical thinking and interpret the results in the context of the research question, domain expertise, and knowledge about the subject matter. This involves considering factors such as the cost and consequences of acting on the findings, the potential impact on stakeholders, and whether the results align with existing theories or evidence.
In summary, practical statistics can outweigh statistical significance by considering effect size and relevance in addition to statistical significance. Considering these aspects provides a deeper understanding of the practical implications and allows for informed decision-making in real-world applications.