Describe how notion of p-value is sometimes misinterpreted and discuss the proper interpretation. 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.
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. 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.
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.
Examples:
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.
Reference:
Scientific method statistical errors
http://www.nature.com/news/scientific-method-statistical-errors-1.14700 (Links to an external site.)
Statisticians issue warning over misuse of P values
http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503 (Links to an external site.)
In statistics, p-value is always used in hypothesis testing. It is always mistakenly interpreted as the probability that the null hypothesis is true. However, p-value does not determine whether the null hypothesis is true or false. Indeed, it should be defined as the probability of obtaining a result at least as extreme as the sample result when assuming the null hypothesis, H0 is true. It is used to quantify the strength of the evidence against the null hypothesis on the side of the alternative hypothesis. A low p-value shows that the sample provides enough evidence to reject the null hypothesis for the population. In comparison, a high p-value shows that the sample does not provide enough evidence to reject the null hypothesis for the entire population. To conclude, p-value does not mean the error rate. Rather, it is a statistical test of the chance that the correlation observed can be due to random variation.
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