![]() ![]() Type I error is the likelihood that the null hypothesis is rejected but should not be. ![]() The chart below summarizes the four scenarios that are possible comparing experimental results (listed on top) with reality (listed on the left): There are two different ways in which an error can be made during hypothesis testing, referred to as Type I error (denoted by α) or Type II error (denoted by β). This experimental determination will either accurately reflect reality or lead to an erroneous conclusion that does not reflect real life. Hypothesis testing refers to the fundamental process of evaluating whether data from one group is either consistent with the null hypothesis (H0) or consistent with an alternative hypothesis (H1). Its primary use is as a tool to be used during study design to determine and justify the appropriateness of a proposed sample size. Power analysis explores the mathematical relationship among several variables involved in study design to inform researchers about its potential to draw meaningful conclusions after data analysis. Of equal importance, however, is that sample size plays a critical role in the inherent ability of a study to detect differences between groups. The desired sample size for a study affects many logistical considerations for research, such as cost projections, resource allocations, and timeframe requirements. When designing a research study, one of the most important considerations is determining the appropriate sample size. ![]()
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