Setting the type I error rate and power should be done in full recognition that they have a substantial effect on the sample size required for the study. On the other hand, if one or both errors would be troublesome but not serious, the type I error rate could be set higher, and the power could be set lower. If failing to find an effect that is real would be serious as well, power should be set high. If a type I error would be serious, that rate should be set low. The type I error rate and desired power should be selected for the study at hand, in consideration of the relative danger of making a type I error or failing to find a real effect. If the consequences of missing a real intervention effect are substantial, an 85% or 90% level of power is sometimes used. In pilot studies, a more relaxed power of 70% is sometimes used. Many view 80% as an acceptable level for power. For pilot studies, a more relaxed type I error rate is sometimes used, such as 10%. If there are two primary outcomes, it is common to divide the 5% type I error rate evenly between the two tests, and to use 2.5% for each one.
The research community has come to look at 5% as an acceptable type I error rate for a single primary outcome.
In general, investigators try to maximize that probability, so that they do not miss a real intervention effect. Power is the probability of correctly rejecting a false null hypothesis. Note: After clicking 'Draw here', you can click the 'Copy to Clipboard' button (in Internet Explorer), or right-click on the graph and choose Copy. We collect evidence to see if the evidence is strong enough to reject the null hypothesis and support the alternative hypothesis. Generally, investigators try to limit that probability, so that they do not report chance findings as though they were real intervention effects. In testing statistical hypothesis, the null hypothesis is first assumed to be true. The type I error rate is the probability of making a type I error, which is to incorrectly reject the null hypothesis of no intervention effect. To set up the test, fill in the boxes: What null hypothesis H 0 about the population proportion p do you want to test Which alternative (this represents the question) is of interest How many observations (n) do you have (30,000 or fewer)If you already have a sample, enter the number of 'successes' to display the sample proportion on the graph and calculate the P-value. You will need to select the type I error rate and desired power for the test of the intervention effect.