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Background
In the previous lessons, we learned about hypothesis testing, which is when we test the results of a research study in the hopes of determining whether the results are meaningful or not. We can think of that as statistical significance. Recall that the power of a test is its sensitivity - a test with high power is able to detect even the smallest deviations from the null hypothesis. Also remember that, in general, a confidence interval is the most commonly-used level of significance in statistical hypothesis testing.
We then learned about null vs. alternative hypotheses and Type I Errors. Recall that Type I error tells us if a finding is a false positive. This lesson will cover Type II Errors the false negatives.
Type II Errors
A Type II Error is a false negative, when we fail to reject the null hypothesis, but the null hypothesis is false. This type of error occurs when the researcher determines that there is no significant result, but there actually is a significant result.
The probability of making a Type II error is also known as . There is a negative correlation between Type I error and Type II error. You can minimize the probability of making a Type II error by increasing your sample size. Reducing the chance of making a Type II error is ideal.
Statistical power is the probability of correctly rejecting the null hypothesis when it is false. We can also think of it as the probability of avoiding a Type II error.
Examples
You go to the doctor to get tested for the flu. The doctor tells you that you tested negative for the flu, but you actually have the flu.
Carl is on trial for grand theft auto. The jury found him not guilty of the crime, but Carl actually did steal the car.
