Type II error
Failing to reject the null hypothesis when it should be rejected, a false negative
Hypothesis testing and inferential statistics are not an exact science. Observation and selection biases associated with data collection and study design can greatly affect the ability to detect true treatment effects in patient populations. A type II error occurs when researchers "do not reject" the null hypothesis (there is no difference or association between variables, or a non-significant finding) when they should "reject" the null hypothesis, meaning that there is a significant difference or association between the variables. Type II errors are also known as "false negatives."
Type II errors are most prevalent when it comes to smaller sample sizes. Small samples sizes often do not all of for enough statistical power to detect significant effects. When non-significant findings are found, it is often wise to run a post hoc power analysis on the yielded values to see what statistical power was achieved. More cases or observations of the outcome may need to be collected for a statistically or clinically meaningful effect can be detected.
Type II errors are most prevalent when it comes to smaller sample sizes. Small samples sizes often do not all of for enough statistical power to detect significant effects. When non-significant findings are found, it is often wise to run a post hoc power analysis on the yielded values to see what statistical power was achieved. More cases or observations of the outcome may need to be collected for a statistically or clinically meaningful effect can be detected.
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