Jove
Visualize
Contact Us
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 Concept Videos

Review and Preview01:10

Review and Preview

8.0K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
8.0K
One-Way ANOVA01:18

One-Way ANOVA

9.3K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
9.3K
Surveys02:16

Surveys

16.1K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
16.1K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

2.9K
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...
2.9K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

10.0K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
10.0K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Assembly of Silphium interspecific hybrid genomes opens the genus to phylogenomics, ecogenomics, and molecular breeding.

Nature communications·2026
Same author

Introgression despite minimal hybridization: mating system modulates phenotypic associations with introgression in Clarkia.

The New phytologist·2026
Same author

Admixture Mapping Reveals Evidence for Multiple Mitonuclear Incompatibilities in Swordtail Fish Hybrids.

Molecular ecology·2025
Same author

Gene flow stops sooner in plants than in animals.

Science (New York, N.Y.)·2025
Same author

Admixture mapping reveals evidence for multiple mitonuclear incompatibilities in swordtail fish hybrids.

bioRxiv : the preprint server for biology·2025
Same author

The first chromosome-scale genome assembly of a microcyclic rust, Puccinia silphii.

BMC genomics·2025

Related Experiment Video

Updated: Oct 18, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.1K

Paired Multiple-Choice Questions Reveal Students' Incomplete Statistical Thinking about Variation during Data

Jenna Hicks1, Jessica Dewey1,2, Michael Abebe1

  • 1Department of Biology Teaching and Learning, University of Minnesota, Minneapolis, Minnesota, USA.

Journal of Microbiology & Biology Education
|October 1, 2021
PubMed
Summary

Students struggle with the quantitative analysis of biological variation. Many can identify mathematical expressions but not interpret them, and understanding p-values differs with sample size, indicating a need for conceptual instruction.

Keywords:
assessmentseducationstatisticsundergraduatevariation

More Related Videos

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

11.6K
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

6.9K

Related Experiment Videos

Last Updated: Oct 18, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.1K
A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

11.6K
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

6.9K

Area of Science:

  • Biology Education Research
  • Quantitative Biology

Background:

  • Understanding biological variability is crucial for data analysis and drawing valid conclusions in biology.
  • Students often face challenges in applying quantitative concepts of variation during statistical analysis.
  • Accurate statistical interpretation is essential for scientific reasoning and evidence-based conclusions.

Purpose of the Study:

  • To assess introductory biology students' understanding of quantitative concepts related to biological variation.
  • To identify specific areas where students struggle in statistically analyzing variation.
  • To inform pedagogical approaches for improving students' quantitative reasoning skills in biology.

Main Methods:

  • Quantitative and qualitative analyses were performed on student responses to multiple-choice questions.
  • The questions targeted two key concepts in the quantitative analysis of variation.
  • Student performance was analyzed based on identifying mathematical expressions and interpreting statistical values.

Main Results:

  • More students correctly identified mathematical expressions of variation than correctly interpreted them.
  • Students were more successful in interpreting nonsignificant p-values with small sample sizes than large ones.
  • A significant portion of students demonstrated an incomplete grasp of quantitative variation concepts.

Conclusions:

  • Introductory biology students exhibit deficits in quantitatively understanding and interpreting biological variation.
  • Instruction should prioritize conceptual understanding of variation over procedural problem-solving to enhance learning.
  • Targeted interventions focusing on statistical reasoning are needed to improve students' quantitative skills.