6. Which of the statements below are correct interpretations of the p-value? You should choose all that are correct interpretations. A. The p-value is the probability of obtaining a sample result at least as or more in favor of the alternative hypothesis if the null hypothesis is true. B. The p-value is the probability that the null hypothesis is true. C. If we repeat the hypothesis test many times, the p-value is the proportion of times our test statistic will be close to the expected value of the null distribution. D. The p-value tells us that the null model is compatible with the data. E. The p-value is the proportion of times in repeated sampling that the alternative hypothesis is true.
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- The p-value is the probability of obtaining a sample result at least as extreme as the observed result, assuming that the null hypothesis is true. Show moreā¦
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Which of the statements below are correct interpretations of the p-value? You should choose all that are correct interpretations. The p-value is the proportion of times in repeated sampling that the alternative hypothesis is true. The p-value is the probability of obtaining a sample result at least as or more in favor of the alternative hypothesis if the null hypothesis is true. This p-value suggests that based on this sample there is strong evidence that the null model is not compatible with the data. This p-value suggests that based on this sample there is little evidence that the null model is not compatible with the data. If we repeat the hypothesis test many times, the p-value is the proportion of times our test statistic will be close to the expected value of the null distribution. The p-value is the probability that the null hypothesis is true.
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Which of the following is a reason to use statistics to evaluate data? a. It enables you to prove that your hypothesis is correct. b. It enables you to exclude data that do not fit your hypothesis. c. It makes it possible to exclude the null hypothesis. d. It enables you to predict experimental results. e. It accounts for variation in scientific measurements.
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