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Related Concept Videos

Decision Making: P-value Method01:09

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...
P-value01:10

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...
Dosage Regimen Designs: Nomograms and Tabulations01:23

Dosage Regimen Designs: Nomograms and Tabulations

Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
Testing a Claim about Population Proportion01:24

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...
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...
Bonferroni Test01:10

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...

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Related Experiment Video

Updated: Jun 15, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

A nomogram for P values.

Leonhard Held1

  • 1Biostatistics Unit, Institute of Social and Preventive Medicine, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland. leonhard.held@ifspm.uzh.ch

BMC Medical Research Methodology
|March 18, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a graphical tool to translate P values and prior probabilities into minimum posterior probabilities. This method aids non-specialists in understanding P values as measures of statistical evidence.

Related Experiment Videos

Last Updated: Jun 15, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Biostatistics
  • Statistical Inference

Background:

  • P values are widely used to assess evidence against hypotheses.
  • Existing methods to convert P values to Bayes factors or posterior probabilities have limited clinical adoption.
  • Barriers include unfamiliarity with Bayes factors and the need for prior probability specification.

Purpose of the Study:

  • To propose a graphical approach for translating P values and prior probabilities into minimum posterior probabilities.
  • To visually demonstrate the relationship between minimum posterior probability and the prior probability of the null hypothesis.
  • To provide a tool for determining compatible prior/posterior probabilities with given P values and vice versa.

Main Methods:

  • A novel nomogram-based graphical method is presented.
  • The approach allows for the direct translation of P values and prior probabilities to minimum posterior probabilities.
  • It facilitates visual inspection of the dependency between prior and posterior probabilities.

Main Results:

  • The graphical tool is illustrated using data from a lung cancer randomized trial comparing radiotherapy techniques.
  • The method provides a clear visualization of how prior beliefs influence posterior probabilities derived from P values.
  • It enables the determination of maximum compatible prior probabilities for a given P value and posterior probability.

Conclusions:

  • The proposed graphical device enhances the interpretation of P values as evidence, particularly for non-specialists.
  • This tool can improve statistical understanding and decision-making in clinical research.
  • It offers a practical solution to the challenges of interpreting P values in a Bayesian context.