The probability of making a Type II error is equal to beta. A Type II error is failure to reject the null when it is false. The power of the test is 1-beta and is the correct decision of rejecting the null when it is false. The alpha level directly affects the power of a test. The higher the level, the more powerful the test. Sample size also affects power. If your question is asking to calculate the actual power of the test, you will need to find methods to calculate beta.
I hope this helps.
A pollster conducts a survey of a random sample of voters in a community to estimate the proportion who support a measure on public health insurance. Let P be the proportion of the population who support the measure. The pollster takes the sample of 200 voters and tests Ho: P=0.5 versus H1:P(not equal)0.5 at 5% level. What is the power of the test if the true value of P is 0.55?
1 answer
1 answer