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Related Concept Videos

Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the population that is...

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Related Experiment Video

Updated: May 9, 2026

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

A note on exact conditional and unconditional tests for Hardy-Weinberg equilibrium.

Guogen Shan1

  • 1Department of Environmental and Occupational Health, Epidemiology and Biostatistics Program, University of Nevada Las Vegas, Las Vegas, Nev., USA.

Human Heredity
|August 8, 2013
PubMed
Summary

The exact conditional approach for Hardy-Weinberg equilibrium testing is computationally intensive. A new unconditional approach offers greater statistical power, especially for two-allele loci, making it a recommended alternative in population genetics.

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Area of Science:

  • Population Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • The exact conditional approach is standard for Hardy-Weinberg equilibrium testing but is computationally demanding.
  • Asymptotic approaches may not control type I error rates effectively.
  • Efficient algorithms exist for the exact conditional method, but alternatives are sought.

Purpose of the Study:

  • To compare the performance of exact conditional and novel unconditional approaches for Hardy-Weinberg equilibrium testing.
  • To evaluate type I error rates and statistical power across different test statistics.
  • To identify a more powerful and practical exact test for population genetic analyses.

Main Methods:

  • Implementation and comparison of exact conditional and two unconditional approaches for Hardy-Weinberg equilibrium.
  • Utilizing three common test statistics for performance evaluation.
  • Assessing type I error rate and statistical power under various conditions.

Main Results:

  • The second unconditional approach, using the conditional p-value as a test statistic, demonstrated uniform power superiority over the conditional test.
  • The unconditional approach based on maximization showed increased power for two-allele loci in small samples.
  • Both unconditional methods were evaluated against the exact conditional test for accuracy and power.

Conclusions:

  • The second unconditional approach is recommended for practical application due to its enhanced power, particularly in scenarios with two alleles.
  • This method provides a more powerful alternative to the traditional exact conditional test for Hardy-Weinberg equilibrium.
  • The findings contribute to more efficient and accurate population genetic analyses.