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Related Concept Videos

Geometric Mean01:15

Geometric Mean

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The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
In cases of multiplicative data, the geometric mean is used for statistical analysis. First, the product of all the elements is taken. Then, if there are n elements in the...
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Trimmed Mean01:10

Trimmed Mean

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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...
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Harmonic Mean01:09

Harmonic Mean

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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
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Median01:08

Median

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Besides mean, the median is a widely used measure of central tendency. Typically, median is defined as the central or middle value of a data set, measured by arranging the data elements in an increasing or decreasing order. Since this middle value is not affected by the precise numerical values of the outliers or fluctuations, it is insensitive to them. Hence, in cases where a data set may have outliers or the extreme values are not known, the median is a better measure of the central tendency...
18.0K
Arithmetic Mean01:08

Arithmetic Mean

13.5K
The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the data set, divided by the total number of elements. It is sometimes loosely referred to as the “average.”
When all the values in a data set are not unique, the sum in the numerator can be calculated by multiplying each distinct value by its frequency.
Sometimes, the arithmetic mean of a sample can be affected by a few data points...
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Updated: Jun 14, 2025

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods.

Jörn Lötsch1,2,3, Dario Kringel1, Alfred Ultsch4

  • 1Institute of Clinical Pharmacology, Goethe University, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany.

Biomedicines
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

The arithmetic mean method for calculating fold change in omics data is unreliable. Robust methods like median or geometric mean improve accuracy and reproducibility in biomedical research.

Keywords:
artificial intelligencedata sciencedifferential expressionomics

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Genomics
  • Proteomics
  • Metabolomics

Background:

  • Fold change is a key metric for analyzing omics data.
  • Inconsistent calculation and reporting of fold change introduce discrepancies.
  • This study addresses the need for standardized fold change methodologies.

Purpose of the Study:

  • To evaluate diverse fold change calculation methods.
  • To identify a preferred, robust approach for omics data analysis.
  • To enhance the reproducibility of biomedical research findings.

Main Methods:

  • Generated artificial datasets with varied distributions (e.g., normal, log-normal).
  • Compared fold change calculations against known values in simulated data.
  • Analyzed a multi-omics dataset to assess real-world applicability.

Main Results:

  • Arithmetic mean-based fold change calculations were frequently inaccurate.
  • Inaccuracies were pronounced with differing subgroup distributions or standard deviations.
  • Alternative methods (median, geometric mean) demonstrated greater robustness.

Conclusions:

  • The arithmetic mean is an inferior method for fold change calculation.
  • Median, geometric mean, or paired fold change methods offer improved reliability.
  • Standardized, robust fold change calculations and transparent reporting are crucial for accurate interpretation and reproducibility.