Kelly performs a hypothesis test and calculates a p-value of .489 for her sample data. What decision can be made about the null hypothesis? 1) fail to reject the null 2) reject the null

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Kelly performs a hypothesis test and calculates a p-value of .489 for her sample data. What decision can be made about the null hypothesis?  1) fail to reject the null  2) reject the null

oces3y · Accepted Answer

Step 1   P-values are the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis.   The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.   A p-value less than 0.05 (typically ≤0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.   However, this does not mean that there is a 95% probability that the research hypothesis is true. The p-value is conditional upon the null hypothesis being true is unrelated to the truth or falsity of the research hypothesis.   A p-value higher than 0.05 (&amp;gt;0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis, we can only reject the null or fail to reject it.   Step 2   In this case since p=0.489&amp;lt;0.05 i.e. the level of significance we have no evidence against our null hypothesis and thus  fail to reject the null hypothesis.

Kelly performs a hypothesis test and calculates a p-value of

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