forum and response 2

Explain the difference but the importance of both statistical significance and practical significance? Statistical significance can be used to study if a decision stemming from a hypothesis test is valid. Practical significance refers to whether a decision based on factual data and customer or internal specifications is valid or acceptable. That is, the statistical difference between two process defect levels is 0.5% (significant) but since the customer or the company allow a defect level of 3%, the practical significance is acceptable. On a different example, the accuracy difference between two pieces of equipment may be of up to 50 microns and considered practically viable while the statistical significance might necessitate a variation of up to 20 microns to verify that the null hypothesis is valid (Munro, 2015, pg.284). Thus, both statistical and practical significance are valid and important. The key is to understand when and how to use or concentrate one or the other. From the previous examples, if a customer is satisfied with a defect level of 3%, the company doesn’t need to spend additional efforts to minimize the statistical significance as their process is already within customer specifications. How do you tell the difference between statistical significance and practical significance? Practical significance can be understood as economic significance which establishes whether a detected sample variation is significant enough to be of practical interest. Statistical significance evaluates the level of accuracy expected of an analysis of data and it’s often interpreted in percent confidence level. A way one could tell the difference between the two is by looking at how is a decision of viability made: was it based upon statistically analyzing the data or was it based on customer or internal economic reasons? ------------------------------------------------------------------------------------- Explain the difference but the importance of both statistical significance and practical significance? As mentioned in an article, “Companies use statistical significance to understand how strongly the results of an experiment, survey, or poll they’ve conducted should influence the decisions they make.” (Gallo, 2016). For Statistical Significance, hypothesis testing is performed where it is determined if the sample results are improbable under the assumption, the null hypothesis is rejected as the data backs up or supports the alternate hypothesis, hence the effect exists. The strength of the evidence or the threshold is defined as the level of significance(alpha) which is usually 0.05. Practical significance, on the other, determines the magnitude of the effect. While no statistical test can determine if the impact of the effect is large enough to be studied, expertise in the area is essential to determine if the effect is worth pursuing and is meaningful. Confidence intervals are used to determine Practical Significance. How do you tell the difference between statistical significance and practical significance? As mentioned in an article, “While statistical significance relates to whether an effect exists, practical significance refers to the magnitude of the effect” (Frost, 2020). Statistical significance is expressed by p-values while effect sizes denote practical significance. This is because statistical significance expresses that an effect exists in a study while practical significance expresses the magnitude of the effect or its application and importance to the real world