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if the cost of a type i error is high, a smaller value should be chosen for the: select one: a. level of significance. b. confidence coefficient. c. critical value. d. test statistic.



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If the cost of a type i error is high, a smaller value should be chosen for the: select one: a. level of significance.

What is a type i error?

A type I (false-positive) error occurs when an investigator rejects a null hypothesis that is actually true in the population; a type II (false-negative) error occurs when an investigator fails to reject a null hypothesis that is actually false in the population.

In machine learning and statistics, Type I and Type II errors are very common. When the Null Hypothesis (H0) is incorrectly rejected, a Type I error occurs. This is also known as the False Positive Error. When a Null Hypothesis that is actually false is accepted, a Type II error occurs.

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