In a test of significance, Type II error is the error of accepting the null hypothesis when it is false — of failing to declare a real difference as statistically significant. Obviously, the bigger your samples, the more likely your test is to detect any difference that exists. The probability of detecting a real difference of specified size (i.e. of not committing a Type II error) is called the power of the test.
Statistics and data science, defined
Type II Error
Where this gets used
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