Jove
Visualize
Contact Us

Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.7K
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).
8.7K
Expected Value01:15

Expected Value

7.8K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
7.8K
What is Variation?01:14

What is Variation?

18.5K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.5K
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

29.6K
The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
29.6K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

28.3K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
28.3K
Induced-fit Model01:13

Induced-fit Model

89.3K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
89.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluation of the replicability of systematic reviews with meta-analyses of the effects of health interventions.

Research synthesis methods·2026
Same author

New approaches to meta-analyze differences in skewness, kurtosis, and correlation.

PLoS biology·2026
Same author

Predator-prey temporal niche partitioning under human disturbance: a meta-analysis.

Nature communications·2026
Same author

Visualization toolkits for enriching meta-analyses through evidence maps, bibliometrics, and alternative impact metrics.

Research synthesis methods·2026
Same author

A practical guide to evaluating sensitivity of literature search strings for systematic reviews using relative recall.

Research synthesis methods·2026
Same author

A comprehensive meta-analysis of exogenous estrogen, progesterone, and testosterone in animal models of ischemic and hemorrhagic stroke.

Biology of sex differences·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Feb 4, 2026

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR
09:03

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Published on: May 29, 2014

12.1K

Gender differences in individual variation in academic grades fail to fit expected patterns for STEM.

R E O'Dea1,2, M Lagisz3, M D Jennions4

  • 1Evolution and Ecology Research Centre, School of Biological and Environmental Sciences, University of New South Wales, Sydney, 2052, NSW, Australia. rose.eleanor.o.dea@gmail.com.

Nature Communications
|September 27, 2018
PubMed
Summary

While girls achieve higher grades, greater male variability in academic performance does not fully explain underrepresentation in science, technology, engineering, and mathematics (STEM) careers. Simulations show equal gender representation in top STEM academic performance.

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K
Skeletal Muscle Gender Dimorphism from Proteomics
09:29

Skeletal Muscle Gender Dimorphism from Proteomics

Published on: December 14, 2011

13.0K

Related Experiment Videos

Last Updated: Feb 4, 2026

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR
09:03

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Published on: May 29, 2014

12.1K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K
Skeletal Muscle Gender Dimorphism from Proteomics
09:29

Skeletal Muscle Gender Dimorphism from Proteomics

Published on: December 14, 2011

13.0K

Area of Science:

  • Educational Psychology
  • Gender Studies
  • Sociology of Science

Background:

  • Despite girls outperforming boys academically, fewer women pursue Science, Technology, Engineering, and Mathematics (STEM) careers.
  • The 'variability hypothesis' posits greater male variance in performance explains male overrepresentation in STEM.
  • Existing research indicates gender disparities in academic achievement and career choices.

Purpose of the Study:

  • To investigate gender differences in academic grades across STEM and non-STEM subjects.
  • To test the 'variability hypothesis' as an explanation for male overrepresentation in STEM.
  • To analyze the impact of gender differences in mean and variance on academic performance thresholds.

Main Methods:

  • Conducted a meta-analysis of academic grades from over 1.6 million students.
  • Compared gender differences in both the mean and variance of grades.
  • Utilized simulations to model the implications of observed gender differences on representation in top academic percentiles.

Main Results:

  • Confirmed lower grade variation and higher average grades for girls compared to boys.
  • Found smaller gender differences in both mean and variance for STEM subjects than non-STEM subjects.
  • Simulations indicated equal numbers of boys and girls in the top 10% of STEM academic performance, but more girls in non-STEM.

Conclusions:

  • Greater male variability in academic grades is insufficient to explain male overrepresentation in STEM fields.
  • Gender differences in academic performance, particularly in variance, are less pronounced in STEM compared to non-STEM subjects.
  • The findings challenge the 'variability hypothesis' as the sole driver for gender disparities in STEM participation.