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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...
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...
Decision Making: Traditional Method01:14

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...
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...
Statistical Significance01:37

Statistical Significance

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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Related Experiment Video

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

A practical solution to the pervasive problems of p values.

Eric-Jan Wagenmakers1

  • 1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. ej.wagenmakers@gmail.com

Psychonomic Bulletin & Review
|December 20, 2007
PubMed
Summary
This summary is machine-generated.

Psychologists often misunderstand p-value null-hypothesis testing, as p-values rely on unobserved data and don't quantify evidence. The Bayesian Information Criterion (BIC) offers a practical solution for statistical inference.

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Area of Science:

  • Psychology
  • Statistics

Background:

  • P-value null-hypothesis testing is a widespread statistical practice in psychology.
  • Many psychologists are unaware of the statistical peculiarities and limitations of p-values.

Purpose of the Study:

  • To review the statistical problems associated with p-values in psychological research.
  • To illustrate these problems with concrete examples.
  • To propose the Bayesian Information Criterion (BIC) as a practical solution.

Main Methods:

  • Review of statistical peculiarities of p-value procedures.
  • Illustration of p-value problems with examples.
  • Introduction of the Bayesian Information Criterion (BIC) for statistical inference.

Main Results:

  • P-values are based on hypothetical, unobserved data influenced by subjective intentions.
  • P-values do not accurately quantify statistical evidence.
  • The BIC approximates Bayesian hypothesis testing without requiring prior specification.

Conclusions:

  • The limitations of p-values necessitate alternative statistical approaches in psychology.
  • The BIC offers a viable and accessible method for statistical inference, calculable from standard statistical software output.