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Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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...
Introduction to the Sign Test01:10

Introduction to the Sign Test

The sign test is an important tool in nonparametric statistics, offering a straightforward yet effective method for analyzing matched pairs, nominal data, or hypotheses concerning the median of a population. It transforms data points into positive or negative signs, avoiding the need for assumptions about data distribution and instead focusing on the direction of change. It is particularly valuable when data does not conform to the normal distribution requirements of many parametric tests. For...
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.
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...

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Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
06:26

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

Published on: November 27, 2019

Introduction to permutation and resampling-based hypothesis tests.

Bonnie J LaFleur1, Robert A Greevy

  • 1Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tuscon, AZ 85724-5163, USA. blafleur@email.arizona.edu

Journal of Clinical Child and Adolescent Psychology : the Official Journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53
|March 14, 2009
PubMed
Summary
This summary is machine-generated.

Permutation tests offer a robust alternative to traditional statistical methods, especially when data assumptions are unmet or outliers are present. These resampling techniques are increasingly accessible and valuable for clinical child and adolescent psychology research.

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Area of Science:

  • Statistics
  • Psychology

Background:

  • Parametric statistical methods rely on specific data distribution assumptions.
  • Violations of these assumptions, common in clinical research, can compromise results.
  • Permutation tests provide a non-parametric alternative.

Purpose of the Study:

  • To provide a tutorial on permutation testing.
  • To highlight the utility of permutation tests in clinical child and adolescent psychology.
  • To demonstrate the application of permutation tests through examples.

Main Methods:

  • Resampling-based inference using permutation tests.
  • Historical overview of permutation testing.
  • Formulation and application examples of permutation tests.

Main Results:

  • Permutation tests are robust to violations of distributional assumptions.
  • These methods are effective in the presence of outliers and missing data.
  • Demonstrated utility in analyzing recent clinical child and adolescent psychology research.

Conclusions:

  • Permutation tests are a valuable and increasingly feasible tool for psychological research.
  • Their robustness makes them suitable for complex datasets often encountered in clinical settings.
  • The accessibility of these methods in statistical software encourages their wider adoption.