Question

Discuss/explain in simple to understand terms what does the p-value represent for any hypothesis test procedure and why is it important to report the p-value when presenting the findings of a test/research. Include in your discussion a specific example you find online for context. (Do not just repeat one similar to another student's example).

Answer 1

P-values determine whether your hypothesus test results are stastically significant. Stastics use them all over the place. You will find p-value in t-tests, distribution tests, ANOVA , and regression analysis. P values have become so important that they have taken on a life of their own. They can determine which studies are published, which projects receive funding, and which university faculty members become tenured. High p-values:your sample results are consistent with a true null hypothesis. Low p- values: your sample results are not consistent with a null hypothesis. If your phone value is small enough, you can conclude that your sample is so incompatiblr with the null hypothesis that you can reject the null for the entire population. P-values are an integral part of inferential stastics because they help you use your sample to draw conclusions about a population. Specifically 4 steps involved in using the p-value approach to conducting any hypothesis test are: 1. Specify the null and alternative hypothesis. 2. Using the sample data and assuming the null hypothesis is true, calculate the value of the test stastic. 3. Using the known distribution of the test stastic, calculate the p-value. 4. Set the significance level, alpha, the probability of making a type 1 error to be small. Compare p-value to alpha.

A stastical test provides a mechanism for making quantitative descions about a process or processes. The purpose is to make inferences about population parameter by analyzing differences between observed sample stastic and the results one expects to obtain if some underlying assumption is true. This comparision may be single observed value two or more related O inrelated groups. The choice of stastical test depends on the nature of the data and the study design. Does p>0.05 mean 'evidence of no difference' between the groups you want to compare? No. P>0.05 only means no evidence of difference. It does not mean evidence of no difference. No evidence of difference does not mean no difference between the groups. We need to consider many contextual factors to derive scientific inferences. Not only p value, but study design the quality of the measurments, and the logical basis of the assumptions are also important.