Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

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...

z Scores and Area Under the Curve

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.

Wilcoxon Signed-Ranks Test for Median of Single Population

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...

Estimating Population Standard Deviation

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA: Equal Sample Sizes

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...

Modified Boxplots

Modified Boxplots

A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...

You might also read

Related Articles

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

Sort by

Same author

Illness-related stigma, mood and adjustment to illness in persons with hepatitis C.

Social science & medicine (1982)·2006

See all related articles

Search research articles

Related Experiment Video

Updated: May 27, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Calculating effect size for continuous variables: D537--a robust, quantile-based approach.

Ronán Michael Conroy¹

¹Royal College of Surgeons in Ireland, Dublin, Ireland. rconroy@rcsi.ie

|November 5, 2011

Summary

This summary is machine-generated.

A new robust effect size formula, D537, is introduced for continuous data. It uses rank statistics to overcome the sensitivity of Cohen

Related Experiment Videos

Last Updated: May 27, 2026

A Tactile Automated Passive-Finger Stimulator (TAPS)

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

Statistics
Biostatistics
Quantitative Research Methods

Background:

Effect size calculation is crucial for assessing intervention significance in continuous data.
Cohen's d, a common effect size measure, is sensitive to outliers, particularly in small studies.
Outlying values can distort the accurate representation of intervention effects.

Purpose of the Study:

To introduce D537, a novel robust formula for calculating effect size with continuous data.
To provide an alternative to Cohen's d that is less susceptible to extreme values.
To ensure accurate effect size estimation even in the presence of outliers.

Main Methods:

The D537 formula utilizes rank statistics for robust estimation.
It employs the median as the measure of difference between groups or paired measurements.
The scaling factor is derived from the interquartile range (30th to 70th percentiles).

Main Results:

D537 provides results equivalent to Cohen's d when data are normally distributed.
The formula demonstrates robustness against outlying values due to its reliance on percentiles.
This method enhances the reliability of effect size calculations in diverse datasets.

Conclusions:

D537 offers a statistically robust alternative for effect size calculation with continuous data.
Its percentile-based approach mitigates the impact of outliers, improving accuracy.
This formula is particularly valuable for small studies or datasets with potential extreme values.