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Related Concept Videos

Variance01:15

Variance

The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.The standard deviation measures the spread in the same units as the data.
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
What is Variation?01:14

What is Variation?

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...
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
One-Way ANOVA01:18

One-Way ANOVA

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...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...

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Related Experiment Video

Updated: Jun 21, 2026

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

Variance analysis as practice-based evidence.

Mark Olive1, Tony Solomonides

  • 1Bristol Institute of Technology, University of the West of England, Bristol, UK. Mark2.Olive@uwe.ac.uk

Studies in Health Technology and Informatics
|July 14, 2009
PubMed
Summary
This summary is machine-generated.

Integrated care pathways (ICPs) can capture valuable practice evidence by analyzing deviations. Feedback from variance analysis improves ICP development, balancing guidelines with clinical freedom.

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Last Updated: Jun 21, 2026

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

  • Healthcare Management
  • Clinical Guidelines
  • Evidence-Based Practice

Background:

  • Integrated care pathways (ICPs) are detailed medical guidelines.
  • ICPs explicitly record deviations, termed 'variance'.
  • Some clinicians perceive ICPs as overly prescriptive, hindering clinical autonomy ('cookbook medicine').

Purpose of the Study:

  • To review research on the development and utilization of ICPs.
  • To present initial findings from a qualitative study on clinician experiences with ICPs.
  • To explore the potential of variance analysis in refining ICPs.

Main Methods:

  • Literature review on ICP development and use.
  • Qualitative study involving clinicians who developed or used ICPs.
  • Analysis of clinician feedback regarding ICPs and variance recording.

Main Results:

  • Clinician perceptions of ICPs vary, with concerns about prescriptive nature.
  • Variance data offers insights into real-world practice and potential guideline improvements.
  • Feedback mechanisms are crucial for effective ICP development and adaptation.

Conclusions:

  • Analyzing variance in ICPs can capture essential practice evidence.
  • This evidence can inform and enhance future ICP development.
  • Balancing standardized pathways with clinical judgment is key for effective healthcare delivery.