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 probability that the null hypothesis is true
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the probability of obtaining the results (or one more extreme) if the null hypothesis is true
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the probability that the alternative hypothesis is true
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probability of a Type I error
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Detailed explanation-1: -The P value is defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance.
Detailed explanation-2: -The p-value is the probability that the null hypothesis is true. (1 – the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.
Detailed explanation-3: -The p value tells you how often you would expect to see a test statistic as extreme or more extreme than the one calculated by your statistical test if the null hypothesis of that test was true.
Detailed explanation-4: -The p-value is the probability of the observed data given that the null hypothesis is true, which is a probability that measures the consistency between the data and the hypothesis being tested if, and only if, the statistical model used to compute the p-value is correct (9).