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Related Concept Videos

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).
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Complementation Tests00:49

Complementation Tests

A complementation test is a simple cross to identify whether the two mutations are located on the same gene or different genes. It was first performed by Edward Lewis in the 1940s while working on fruit flies. He developed the test to identify the location and arrangement of different mutations on chromosomes.
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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...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Sign Test for Nominal Data01:12

Sign Test for Nominal Data

The sign test is a nonparametric method used to evaluate hypotheses about the median of a single sample or to compare the medians of two related samples. The sign test is particularly useful when dealing with nominal data, which includes distinct categories without an inherent order, such as names, labels, and preferences. Nominal data restricts statistical analysis to evaluating population proportions rather than mean or median values that require continuous data.
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Related Experiment Video

Updated: May 22, 2026

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

Color Trails Test: regression-based norms for Russian-speakers.

Zarui A Melikyan1, Jason R Bock1, Anna V Agranovich2

  • 1Department of Neurobiology and Behavior, University of California, Irvine, CA, USA.

Archives of Clinical Neuropsychology : the Official Journal of the National Academy of Neuropsychologists
|May 20, 2026
PubMed
Summary
This summary is machine-generated.

New regression-based norms for the Color Trails Test (CTT) improve accuracy for Russian-speaking adults. These norms identify fewer individuals as performing below average compared to standard manual norms.

Keywords:
CTT normsColor Trails Testculture-sensitive normsnorms for Russian-speakersregression-based norms

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Last Updated: May 22, 2026

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Published on: June 30, 2020

Area of Science:

  • Neuropsychological assessment
  • Cognitive psychology
  • Cross-cultural psychology

Background:

  • The Color Trails Test (CTT) is a widely used neuropsychological tool.
  • Cultural variations necessitate culture-specific norms for accurate test interpretation.
  • Existing CTT norms are unavailable for Russian-speaking populations.

Purpose of the Study:

  • Establish regression-based norms (RBNs) for the CTT in Russian-speaking adults.
  • Compare CTT T-scores derived from RBNs versus standard manual norms.

Main Methods:

  • Administered the CTT to 196 healthy Russian-speaking adults.
  • Utilized multiple linear regression to develop RBNs.
  • Calculated and compared T-scores using RBNs and manual norms via t-tests.

Main Results:

  • CTT completion time correlated with age and education, not sex.
  • Regression-based norms yielded higher CTT1 T-scores than manual norms (p < .001).
  • Fewer participants were identified as below average using RBNs (7% vs. 18% for CTT1; 7% vs. 10% for CTT2).

Conclusions:

  • The developed RBNs enhance the accuracy of CTT assessments in Russian-speaking adults.
  • Improved diagnostic outcomes are anticipated with the use of these culture-relevant norms.