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How do you determine if the null hypothesis is rejected or accepted?
If the P-value is less than (or equal to) , then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P-value is greater than , then the null hypothesis is not rejected.
What is the criteria for rejecting the null hypothesis?
To reject the null hypothesis, the p-value must be less than alpha. In our example, if we obtain a sample mean of 550, the p-value is the probability of observing a mean as large or larger than 550 if the population mean really is only 500. The p-value is not the probability that the null hypothesis is true.
Which of the following sample size makes it easier to reject the null hypothesis?
Increasing sample size
The correct answer is (C). Increasing sample size makes the hypothesis test more sensitive – more likely to reject the null hypothesis when it is, in fact, false.
How is sampling error used in null hypothesis testing?
Similarly, the correlation (Pearson’s r) between two variables might be +.24 in one sample, −.04 in a second sample, and +.15 in a third—again, even though these samples are selected randomly from the same population. This random variability in a statistic from sample to sample is called sampling error.
Why does sample size and effect size increase the power of a statistical test?
How we test this hypothesis is that we calculate the test statistics and p value then compare with α. As the sample size gets larger, the z value increases therefore we will more likely to reject the null hypothesis; less likely to fail to reject the null hypothesis, thus the power of the test increases.
When to reject the null hypothesis in a population?
There is no relationship between the variables in the population. Determine how likely the sample relationship would be if the null hypothesis were true. If the sample relationship would be extremely unlikely, then reject the null hypothesis in favor of the alternative hypothesis.
How to calculate Sample Size for hypothesis testing?
We compute the sample mean and then must decide whether the sample mean provides evidence to support the alternative hypothesis or not. This is done by computing a test statistic and comparing the test statistic to an appropriate critical value.