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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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Fisher's Exact Test01:08

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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...
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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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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Bonferroni Test01:10

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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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Significance Testing: Overview01:04

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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: Jan 19, 2026

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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To test or to estimate? P-values versus effect sizes.

Daniela Dunkler1, Maria Haller1,2, Rainer Oberbauer3

  • 1Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

Transplant International : Official Journal of the European Society for Organ Transplantation
|September 28, 2019
PubMed
Summary
This summary is machine-generated.

Statistical analysis in transplant medicine should report effect sizes and confidence intervals, not just P-values. This approach provides a clearer understanding of clinical relevance, especially in observational studies.

Keywords:
clinical significanceeffect size measurestatistical inferencestatistical significancestatistical tests

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Area of Science:

  • Transplant Medicine
  • Biostatistics
  • Clinical Research

Background:

  • Statistical analysis is integral to transplant medicine research.
  • Over-reliance on P-values for significance is common but problematic, particularly in observational studies.
  • P-values alone can lead to inaccurate conclusions regarding intervention effects.

Purpose of the Study:

  • To advocate for improved statistical reporting in transplant medicine.
  • To emphasize the importance of effect size measures and confidence intervals.
  • To guide researchers toward more accurate interpretations of study findings and clinical relevance.

Main Methods:

  • Discusses the limitations of P-value-centric statistical analysis.
  • Recommends reporting effect size measures (e.g., risk difference, relative risk, hazard ratio).
  • Stresses the necessity of accompanying effect sizes with precision estimates, such as 95% confidence intervals.

Main Results:

  • P-values alone are insufficient for determining the significance and impact of interventions.
  • Effect size measures quantify the magnitude of an observed effect.
  • Confidence intervals provide a range of plausible values for the true effect size.

Conclusions:

  • Reporting effect sizes with confidence intervals enhances the interpretation of clinical relevance.
  • This dual approach offers a more robust understanding of research outcomes in transplant medicine.
  • Moving beyond P-values leads to more reliable and clinically meaningful conclusions.