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P-value

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value. P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more unlikely...

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

Bonferroni Test

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...

Identifying Statistically Significant Differences: The F-Test

Identifying Statistically Significant Differences: The F-Test

The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...

Fisher's Exact Test

Fisher's Exact Test

Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of the...

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Why do we worship P < .05?

Duncan Neuhauser¹, Lloyd Provost

¹Department of Epidemiology and Biostatistics, Medical School, Case Western Reserve University, Cleveland, Ohio 44106, USA. dvn@case.edu

Quality Management in Health Care

|December 31, 2011

Summary

This summary is machine-generated.

The P value, a statistical measure, has become overly influential in medical research, impacting drug approvals and scientific careers. This overreliance on P < .05 may distort medical practice and scientific integrity.

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Area of Science:

Medical research methodology
Statistical significance in clinical trials

Background:

The P value is a common metric for statistical significance in scientific studies.
Its widespread use influences critical decisions in medicine, from drug approval to academic advancement.

Purpose of the Study:

To explore the historical and practical reasons behind the P value's prominent role in medical practice.
To critically examine the implications of over-reliance on P < .05 for scientific integrity and patient outcomes.

Main Methods:

Historical analysis of statistical practices in medical research.
Review of regulatory and publication guidelines concerning P values.
Discussion of the impact of P value thresholds on research interpretation.

Main Results:

The P value gained prominence due to its utility in early statistical inference and its adoption by influential journals.
Significant financial and career incentives are tied to achieving P < .05, creating a bias towards "positive" results.
Overemphasis on the P value can lead to misinterpretation of results and potentially flawed medical practices.

Conclusions:

The pervasive influence of the P value in medicine warrants critical re-evaluation.
A shift towards more comprehensive statistical reporting and interpretation is needed to ensure robust and reliable medical advancements.