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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
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Percentile01:18

Percentile

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A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
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Comparing Experimental Results: Student's t-Test01:09

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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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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).
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Sign Test for Nominal Data01:12

Sign Test for Nominal Data

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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.
For example, consider a...
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Related Experiment Video

Updated: Apr 11, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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The NIH Toolbox Pattern Comparison Processing Speed Test: Normative Data.

Noelle E Carlozzi1, Jennifer L Beaumont2, David S Tulsky3

  • 1Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, USA carlozzi@med.umich.edu.

Archives of Clinical Neuropsychology : the Official Journal of the National Academy of Neuropsychologists
|May 31, 2015
PubMed
Summary
This summary is machine-generated.

The NIH Toolbox Pattern Comparison Processing Speed Test is reliable for ages 5-85, showing good validity but a moderate practice effect over one week. This processing speed measure is suitable for lifespan research.

Keywords:
CognitionNIH ToolboxNeuropsychological assessmentPattern Comparison Processing Speed TestProcessing speed

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Area of Science:

  • Cognitive Psychology
  • Neuropsychology
  • Psychometrics

Background:

  • The NIH Toolbox Pattern Comparison Processing Speed Test aims to measure cognitive processing speed.
  • Preliminary validation exists, but further research is needed for dependability and generalizability across diverse age groups.

Purpose of the Study:

  • To conduct a replication study examining the NIH Toolbox Pattern Comparison Processing Speed Test.
  • To assess descriptive data, age effects, test-retest reliability, and construct validity in a large, demographically matched sample.

Main Methods:

  • A large-scale replication study involving 4,859 participants aged 3-85 years.
  • Data collection included descriptive statistics, test-retest reliability assessments over a 1-week interval, and construct validity analyses (convergent and discriminant).

Main Results:

  • The test was not suitable for all 3- and 4-year-olds, but yielded meaningful scores for ages 5 and 6.
  • Evidence for convergent and discriminant validity was found.
  • A moderate practice effect (5.5-point increase) was observed over a 1-week retest interval.

Conclusions:

  • The NIH Toolbox Pattern Comparison Processing Speed Test demonstrates strengths including lifespan applicability (ages 5-85), ease of administration, and supported construct validity.
  • Findings support its use as a measure of processing speed, with considerations for younger children and potential practice effects.