power and confidence level of research
- related: Biostats
- tags: #literature #pulmonary
Hypothesis testing is important when performing empirical research and evidence-based medicine. There are two types of statistical hypotheses: the null and the alternative hypotheses. The null hypothesis is denoted by H0 and it is usually the hypothesis that sample observations result purely from chance. The alternative hypothesis, denoted by H1 or Ha, is the hypothesis that sample observations are influenced by some nonrandom causes. In any hypothesis testing, there are four possible outcomes (Figure 1). The power of the test is the probability that the test will reject the null hypothesis when, in fact, it is false. Having a high value for 1 − β (near 1.0) means it is a good test, and having a low value (near 0.0) means it is a poor test. Conventionally, a test with a power of 0.8 is considered acceptable. In addition, when a test accepts the null hypothesis when it happens to be true, it is called confidence level denoted by (1 − α). A Type I error (also called the significance level and is usually denoted by α) occurs when the researcher rejects a null hypothesis when it is true. A Type II error (also called β) occurs when the researcher accepts the null hypothesis that is false.1