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Decision Making: P-value Method

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...

P-value

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...

Accuracy and Errors in Hypothesis Testing

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...

Errors In Hypothesis Tests

Errors In Hypothesis Tests

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.

Decision Making: Traditional Method

Decision Making: Traditional Method

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...

Critical Region, Critical Values and Significance Level

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...

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Related Experiment Video

Updated: Dec 31, 2025

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI

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Published on: November 27, 2019

A Reckless Guide to P-values : Local Evidence, Global Errors.

Michael J Lew¹

¹Department of Pharmacology and Therapeutics, University of Melbourne, Parkville, VIC, Australia. michaell@unimelb.edu.au.

Handbook of Experimental Pharmacology

|January 4, 2020

Summary

This summary is machine-generated.

This study clarifies P-values and hypothesis testing, emphasizing the combined use of local evidence and global error rates for robust scientific inference. Replication is key to resolving conflicts in statistical analysis.

Keywords:

Evidence Hypothesis test Multiple testing P-hacking P-values Scientific inference Significance filter Significance test Statistical inference

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Area of Science:

Statistics in scientific research
Pharmacology and experimental design

Background:

P-values, hypothesis tests, and significance tests are fundamental but often misunderstood statistical concepts.
Local evidence and global error rates are critical components of statistical inference, representing data-specific findings and procedural accuracy, respectively.

Purpose of the Study:

To demystify P-values and hypothesis testing for basic pharmacologists.
To explain the interplay between local evidence and global error rates in scientific inference.
To address issues like multiple testing, HARKing, and P-hacking and their impact on statistical validity.

Main Methods:

Introduction of local evidence and global error rates with simple examples.
Explanation of power analysis for experimental design in hypothesis testing.
Discussion of expected P-values for focused local evidence.

Main Results:

Demonstration that local evidence and global error rates should be considered together for valid inferences.
Explanation of how multiple testing, HARKing, and P-hacking can conflict with local evidence and global error rates.
Highlighting replication as a method to overcome these conflicts.

Conclusions:

P-values serve as valuable indices of evidence in statistical toolboxes.
Statistical inference calibrates uncertainty but does not replace scientific inference.
Understanding the balance between local evidence and global error rates enhances the reliability of scientific findings.