METHODS OF DATA ANALYSIS
DECISION MAKING WITH HYPOTHESIS TESTING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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The failure to reject a false null hypothesis
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Rejecting a true null hypothesis
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The acceptance to reject a null hypothesis
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Accepting a true null hypothesis
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Detailed explanation-1: -A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.
Detailed explanation-2: -What Is a Null Hypothesis? A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations. Hypothesis testing is used to assess the credibility of a hypothesis by using sample data.
Detailed explanation-3: -1) Type-I Error: In a hypothesis test, a Type-I error occurs when the null hypothesis is rejected when it is in fact true. That is, H0 is wrongly rejected.
Detailed explanation-4: -Key Takeaways Type I and Type II errors are very common in machine learning and statistics. Type I error occurs when the Null Hypothesis (H0) is mistakenly rejected. This is also referred to as the False Positive Error. Type II error occurs when a Null Hypothesis that is actually false is accepted.