In an attempt to reduce the likelihood of a type ii error, the experimenter proposes to recruit a very large group of participants.
In statistical hypothesis testing, a Type I error is actually an incorrect rejection of the true null hypothesis (a.k.a. a "false positive" result or conclusion; e.g., "Innocent person convicted ing"). Rejection of one actually false null hypothesis (also called a "false negative" result or conclusion, e.g. "guilty party not convicted").
Many statistical theories revolve around minimizing one or both of these errors, but unless the outcome is determined by a known and observable causal process, either of these errors can be completely quantified. It is statistically impossible to eliminate You can improve the quality of the hypothesis test by choosing a lower threshold (cutoff) and changing the alpha (α) level. Knowledge of type I and type II errors is widely used in medicine, biometrics, and computer science.
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