# Describe how notion of p-value is sometimes misinterpreted and discuss the proper interpretation.Research this question in any way that you wish. You might find the following reference useful.

Describe how notion of p-value is sometimes misinterpreted and discuss the proper interpretation.Research this question in any way that you wish. You might find the following reference useful.
Discussion #3
The notion of p-value is very often misunderstood. Describe how notion of p-value is sometimes misinterpreted and discuss the proper interpretation. Research this question in any way that you wish. You might find the following reference useful.
http://www.amstat.org/asa/files/pdfs/P-ValueStatement.pdf
http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503
http://www.stat.ualberta.ca/~hooper/teaching/misc/Pvalue.pdf
Note: For any of the assignments or discussion forum posts, you are able to submit the equations in your text responses by using the Pi symbol () in the text editor window.
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P-value is a statistic which is used to measure the degree of how extreme the observation is. It is a very common function in statistical hypothesis testing, specifically in null hypothesis significance testing. The definition of p-value is the probability of obtaining a result equal to or “more extreme” than what was actually observed, when the null hypothesis is true. In normal statistical hypothesis testing, p-value can be a indicator to decide whether to reject null hypothesis or not. When p-value is less than the required significance level, we reject null hypothesis.
While p-value is useful, many people just misused it. First, P-value is based on null- hypothesis, it can’t be used to evaluate null-hypothesis. The P value cannot say this: all it can do is summarize the data assuming a specific null hypothesis. It cannot work backwards and make statements about the underlying reality. That requires another piece of information: the odds that a real effect was there in the first place. Second, a P value of 0.05 does not mean that there is a 95% chance that a given hypothesis is correct. Instead, it signifies that if the null hypothesis is true, and all other assumptions made are valid, there is a 5% chance of obtaining a result at least as extreme as the one observed. Third, a P value can only demonstrate the result statistically not in reality. For example, a drug can have a statistically significant effect on patients’ blood glucose levels without having a therapeutic effect. There are more misinterpretations of p-value and we should be prudent when use it.

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