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Related Concept Videos

P-value01:10

P-value

9.3K
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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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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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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Statistical Significance01:37

Statistical Significance

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
23.4K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.0K
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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Significance Testing: Overview01:04

Significance Testing: Overview

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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Related Experiment Video

Updated: Mar 17, 2026

Author Spotlight: Advancing Caenorhabditis elegans Research Using Paraformaldehyde-Treated Bacteria
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The Value of the P Value.

Dinesh Vyas1, Archana Balakrishnan1, Arpita Vyas1

  • 1College of Human Medicine, Michigan State University, East Lansing, MI 48824, USA.

American Journal of Robotic Surgery
|July 19, 2016
PubMed
Summary

Scientific irreproducibility is a major concern, often linked to the misuse of p-values in null hypothesis testing. This research explores causes, impacts on drug development, and solutions for greater research transparency.

Area of Science:

  • Scientific research methodology
  • Biostatistics
  • Drug development

Background:

  • Irreproducibility in scientific research is a widely discussed issue.
  • The overuse of p-values for determining statistical significance is a frequently cited cause.
  • Concerns exist regarding the reliance on null hypothesis testing in scientific analysis.

Purpose of the Study:

  • To survey current issues surrounding research irreproducibility.
  • To identify potential causes contributing to the irreproducibility crisis.
  • To examine the impact of irreproducibility on drug development and explore transparency efforts.

Main Methods:

  • Literature review of current issues in scientific irreproducibility.
  • Analysis of the role of p-values and null hypothesis testing.
Keywords:
Data AnalysisIrreproducibility in ResearchP ValueStatistical ModelingStatisticsp-Value

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  • Examination of impacts on pharmaceutical research and development.
  • Review of strategies to enhance research transparency.

    • Main Results:

    • Overreliance on p-values and null hypothesis testing are significant contributors to irreproducibility.
    • Research irreproducibility poses risks to the efficiency and reliability of drug development.
    • Various initiatives are underway to promote transparency in scientific findings.

    Conclusions:

    • Addressing the misuse of statistical significance is crucial for improving research reproducibility.
    • Enhanced transparency in research is essential for scientific integrity and progress, particularly in drug development.
    • Continued efforts are needed to reform statistical practices and foster a more reproducible scientific landscape.