A Type II error is a false negative in a test outcome, where something is falsely inferred to not exist. This usually means incorrectly accepting the null hypothesis (H0), which is the testing statement that whatever is being studied has no statistically significant effect on the problem.

### Null Hypothesis and Statistical Significance

In practice, the difference between a false positive and a false negative is usually not so clear-cut. Since the tests are most often quantitively rather than qualitatively based, the results tend to be expressed in a confidence interval value less than 100%, rather than a simple Yes/No decision. This question of how likely the results are to be found if the null hypothesis is true is called statistical significance.

A simple calculation is run to determine this significance. The probability of the test rejecting the null hypothesis if it were true is expressed as Î±. This is given a pre-defined minimum value, usually 5%, before the test is conducted. The probability of obtaining an equal or greater result if the null hypothesis were true is expressed as the p-value.

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