The concepts of Type 1 error, Type 2 error, and test power are of great importance in the research we conduct, but unfortunately, they can be a bit challenging to understand.
Type 1 error expresses the probability of claiming that there is an effect/relationship when there isn't one in reality and is referred to as the test's error rate. It's also called the significance level and is generally accepted as no more than 0.05 or 5%. When determining the sample size, as n increases beyond what is necessary, the probability of making a Type 1 error increases.
Type 2 error, on the other hand, is the probability of failing to detect an effect/relationship that actually exists. It is generally accepted as no more than 0.20 or 20%. When determining the sample size, if n is insufficient, the probability of making a Type 2 error increases.
Finally, test power indicates the probability of finding a significant result in the test. It is determined by the formula "1 - Type 2 error." Therefore, since Type 2 error is accepted as no more than 0.20, the test power should be at least 0.80 (80%).
You can watch the details of the topic in our video:
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