B.2 concept of power
- 1. Concept of Power of Hypothesis Test
- 2. Concept of Power of Hypothesis Testing • Power of Hypothesis Test probability of not committing a Type II Error • Effect Size: the difference between the true values and the value from the null hypothesis. – The true value is an alternative value of the population parameter, assuming that the null hypothesis is false – EFFECT SIZE= TRUE VALUE- HYPOTHESIZED VALUE – Example: if the Ho says that the population mean is 20 and a statistician asks “what is the probability of rejecting the null hypothesis if the true population mean is 15?” Then the effect size would be: 15-20= =-5
- 3. Factors that Affect Power • Power of hypothesis test is affected by 3 factors: 1. Sample size (n)- the greater the sample size, the greater the power 2. Significance level (α)- the higher the α, the higher the power of the test *If you increase the significant level, the region of acceptance is reduced. Which means it becomes more likely to reject the null * This then makes it less likely to not reject the null hypothesis when it is false, which means the less likely it is to make a Type II error, resulting in the power of the test to be increased. 3. “true” value of the parameter- the greater the effect size, the greater the power of the test