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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

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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...
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P-value01:10

P-value

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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...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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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...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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Bonferroni Test01:10

Bonferroni Test

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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...
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Three common misuses of P values.

Jeehyoung Kim1, Heejung Bang2

  • 1Department of Orthopedic Surgery, Seoul Sacred Heart General Hospital, Seoul, Korea.

Dental Hypotheses
|October 4, 2016
PubMed
Summary

The p-value, a measure of statistical significance, is often overemphasized in research. This article clarifies its definition and common misuses for clinicians and researchers.

Area of Science:

  • Statistics
  • Scientific Research Methodology

Background:

  • The p-value is a standard measure of statistical significance used in hypothesis testing.
  • Its role in scientific inference has become disproportionately emphasized.
  • Recent concerns have led to calls for re-evaluating its use, including a statement from the American Statistical Association.

Purpose of the Study:

  • To review the precise statistical definition of the p-value.
  • To identify and discuss common misinterpretations and misuses of p-values in scientific literature.
  • To provide guidance for clinicians and researchers on appropriate use and interpretation.

Main Methods:

  • Literature review and conceptual analysis of statistical significance.
  • Examination of historical context and evolution of p-value usage.
Keywords:
confidence intervalmultiple testingp-value plot

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  • Synthesis of recommendations from statistical and scientific bodies.

    • Main Results:

    • P-values have a clear mathematical definition but are frequently misinterpreted.
    • Over-reliance on p-values can lead to flawed scientific conclusions.
    • The American Statistical Association has issued guidance to address these issues.

    Conclusions:

    • Correct interpretation of p-values is crucial for sound statistical inference.
    • Researchers should be aware of the limitations and potential misuses of p-values.
    • This review aims to improve the understanding and application of p-values in clinical and scientific research.