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Related Concept Videos

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.
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...
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...
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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
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...

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Published on: March 1, 2022

Evaluating order-constrained hypotheses for circular data using permutation tests.

Irene Klugkist1, Jessie Bullens, Albert Postma

  • 1Department of Methodology and Statistics, Utrecht University, The Netherlands. I.Klugkist@uu.nl

The British Journal of Mathematical and Statistical Psychology
|May 13, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces three new permutation tests for analyzing directional data in psychology. These tests improve statistical power for ordered hypotheses compared to standard methods.

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Area of Science:

  • Psychology
  • Statistics

Background:

  • Psychological research frequently involves directional or circular data, such as angles or orientations.
  • Examples include spatial memory development and haptic perception influenced by visual experience.

Purpose of the Study:

  • To propose and evaluate novel permutation tests for ordered hypotheses with circular data.
  • To enhance statistical power in analyzing directional psychological data.

Main Methods:

  • Development of three permutation tests designed for ordered hypotheses.
  • Simulation studies were conducted to assess the performance and power of these new tests.
  • Comparison against a common non-directional test for circular data.

Main Results:

  • The proposed permutation tests demonstrated increased statistical power for ordered hypotheses.
  • This improvement was evident when compared to standard non-directional tests for circular data.
  • Power differences among the three proposed tests were minimal.

Conclusions:

  • The new permutation tests offer a more powerful approach for analyzing ordered directional data in psychological research.
  • These methods provide a valuable tool for researchers dealing with circular data, enhancing analytical capabilities.