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Related Concept Videos

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...
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...
Joints01:26

Joints

Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

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...
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
McNemar's Test01:23

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...

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Related Experiment Video

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Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model
07:32

Standardized Histomorphometric Evaluation of Osteoarthritis in a Surgical Mouse Model

Published on: May 6, 2020

Standardization of joint examination technique leads to a significant decrease in variability among different

Mathias Grunke1, Christian E Antoni, Arthur Kavanaugh

  • 1Unit for Rheumatology, Ludwig-Maximilians-University, Munich, Germany. mathias.gruenke@med.uni-muenchen.de

The Journal of Rheumatology
|February 17, 2010
PubMed
Summary
This summary is machine-generated.

Standardized training for rheumatoid arthritis (RA) joint examinations significantly reduced variability among assessors. This improvement enhances the reliability of clinical trial data for RA patients globally.

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Area of Science:

  • Rheumatology
  • Clinical Trials
  • Medical Education

Background:

  • Variability among assessors in clinical trials can impact data reliability.
  • Standardized examination techniques are crucial for consistent rheumatoid arthritis (RA) assessments.
  • European League Against Rheumatism (EULAR) guidelines provide a basis for standardized joint examinations.

Purpose of the Study:

  • To reduce inter-assessor variability in joint examinations for rheumatoid arthritis (RA) clinical trials.
  • To evaluate the effectiveness of joint examination standardization seminars.
  • To implement EULAR-recommended examination techniques in multicenter trials.

Main Methods:

  • Conducted joint examination standardization seminars for multicenter RA trials.
  • Participants performed joint examinations before and after training on EULAR techniques.
  • Compared the variance in joint assessments (tender, swollen) among examiners pre- and post-training.

Main Results:

  • A substantial reduction in inter-assessor variability was observed after training.
  • The number of joints identified as active was significantly reduced post-training.
  • 553 health professionals from diverse global regions participated.

Conclusions:

  • Standardized joint examination training demonstrably reduces variability among assessors.
  • This standardization enhances the consistency and reliability of RA assessments in clinical settings.
  • The findings support the adoption of EULAR-based training for multicenter RA studies.