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
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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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