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

Sampling Distribution01:12

Sampling Distribution

18.7K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
18.7K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.2K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.2K
Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis01:24

Microsoft Excel: Finding Central Tendency, Skew, and Kurtosis

768
Central tendency refers to the central point or typical value of a dataset. It summarizes the data set with a single value that represents the center of its distribution. The three main measures of central tendency are:
Mean: The arithmetic average of all data points. It is calculated by adding all the values together and dividing by the number of values. The mean is sensitive to extreme values (outliers).
Median: The middle value when the data points are arranged in ascending or descending...
768
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.3K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

29.7K
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...
29.7K
Trimmed Mean01:10

Trimmed Mean

3.6K
While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
3.6K

You might also read

Related Articles

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

Sort by
Same author

Ecology needs a causal overhaul.

Biological reviews of the Cambridge Philosophical Society·2025
Same author

Refining our understanding of the diversity of plant specialised metabolites.

The New phytologist·2024
Same author

The demography of human warfare can drive sex differences in altruism.

Evolutionary human sciences·2023
Same author

The price of defence: toxins, visual signals and oxidative state in an aposematic butterfly.

Proceedings. Biological sciences·2023
Same author

Artificial light at night may decrease predation risk for terrestrial insects.

Biology letters·2022
Same author

An ecological perspective on water shedding from leaves.

Journal of experimental botany·2021

Related Experiment Video

Updated: Mar 8, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.7K

Testing for departure from uniformity and estimating mean direction for circular data.

Graeme D Ruxton1

  • 1School of Biology, University of St Andrews, St Andrews KY16 9TH, UK graeme.ruxton@st-andrews.ac.uk.

Biology Letters
|January 20, 2017
PubMed
Summary

Biological studies often use circular data, but analyses can be improved. This research demonstrates rigorous methods for analyzing circular data, revealing biological insights missed by simpler approaches.

Keywords:
circular dataconfidence intervalnull hypothesis testingtesting for a specified mean direction

More Related Videos

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

13.4K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.7K

Related Experiment Videos

Last Updated: Mar 8, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.7K
Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

13.4K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.7K

Area of Science:

  • Ecology
  • Statistics
  • Biology

Background:

  • Circular data analysis in biology is often rudimentary.
  • A common hypothesis tests directional clustering versus uniform distribution.

Purpose of the Study:

  • To demonstrate improved rigor in analyzing circular biological data.
  • To highlight broader hypothesis testing and confidence interval estimation methods.
  • To provide guidance on selecting appropriate statistical methods for circular data.

Main Methods:

  • Utilized data on epiphyte and mistletoe compass directions on tree trunks.
  • Applied advanced statistical methods for circular data analysis.
  • Focused on hypothesis testing and confidence interval estimation for mean direction.

Main Results:

  • Showcased that a wider range of null hypotheses can be tested.
  • Identified various methods for estimating confidence intervals for mean direction.
  • Emphasized the necessity of correcting for biased sample estimates in circular data.

Conclusions:

  • Enhanced statistical rigor in circular data analysis yields deeper biological insights.
  • Researchers can improve hypothesis testing and parameter estimation for circular data.
  • Correcting for bias in sample estimates is crucial for accurate population parameter inference.