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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

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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

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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.
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Decision Making: Traditional Method01:14

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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.
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Statistical Significance01:37

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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...
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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.
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The debate about p-values.

Ying Lu1, Ilana Belitskaya-Levy2

  • 1VA (Veterans Affairs) Cooperative Studies Program Palo Alto Coordinating Center, VA Palo Alto Health Care System, Palo Alto, CA, USA.

Shanghai Archives of Psychiatry
|May 21, 2016
PubMed
Summary
This summary is machine-generated.

The p-value is crucial in biomedical research but has limitations. Misinterpreting p-values can affect research reproducibility, necessitating careful interpretation alongside other data for valid conclusions.

Keywords:
hypothesis testinginferential statisticsp-valuescientific repeatabilitystatistical significance

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Area of Science:

  • Biostatistics
  • Biomedical Research Methodology
  • Clinical Trials

Background:

  • The p-value is a fundamental statistical tool in biomedical research.
  • Controversies exist regarding p-value utility and its link to research irreproducibility.
  • Understanding p-value limitations is critical for accurate interpretation of study findings.

Purpose of the Study:

  • To explain the p-value in simple terms, detailing its inherent randomness and limitations.
  • To discuss the limitations of p-value alternatives.
  • To clarify the appropriate use of p-values in clinical trials and research evaluation.

Main Methods:

  • Conceptual explanation of p-value statistics.
  • Discussion of p-value limitations and alternatives.
  • Review of p-value application in hypothesis testing for clinical trials.

Main Results:

  • P-values demonstrate randomness and have inherent limitations.
  • Alternatives to p-values share similar limitations.
  • Single p-values are insufficient for judging the scientific merit of research.

Conclusions:

  • P-values are valid for testing null and alternative hypotheses in clinical trials.
  • Misuse of p-values occurs when judging scientific merit solely on a single test.
  • Clinically valid conclusions require integrating p-value results with secondary data and other evidence.