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Related Concept Videos

z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

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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...
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Introduction to z Scores01:05

Introduction to z Scores

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A z score (or standardized value) is measured in units of the standard deviation. It indicates 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 zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
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Introduction to z Scores01:06

Introduction to z Scores

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A z score (or standardized value) is measured in units of the standard deviation. It 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 zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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z Scores and Unusual Values01:07

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The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data...
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Testing a Claim about Standard Deviation01:19

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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.
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Related Experiment Video

Updated: Mar 24, 2026

Sperm Collection of Differential Quality Using Density Gradient Centrifugation
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Reducing Inter-Laboratory Differences between Semen Analyses Using Z Score and Regression Transformations.

Esther Leushuis1, Alex Wetzels2, Jan Willem van der Steeg3

  • 1Department of Obstetrics and Gynecology, Vrije Universiteit Medical Center, Amsterdam, The Netherlands; Academic Medical Center, Center for Reproductive Medicine, Amsterdam, The Netherlands.

International Journal of Fertility & Sterility
|March 18, 2016
PubMed
Summary
This summary is machine-generated.

Standardizing semen analysis with Z scores did not reduce laboratory variability in sperm concentration or morphology. Significant differences between labs persist, impacting test reproducibility.

Keywords:
DifferencesRegressionSemen AnalysisStandardization

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Area of Science:

  • Reproductive Medicine
  • Clinical Laboratory Science
  • Andrology

Background:

  • Semen analysis standardization is crucial for improving the reproducibility of results.
  • Significant variability exists between laboratories performing semen analyses.

Purpose of the Study:

  • To assess between-laboratory variability in semen analyses.
  • To evaluate the effectiveness of Z-score and regression-based standardization in reducing this variability.

Main Methods:

  • Retrospective cohort study analyzing semen parameters.
  • Calculation of between-laboratory coefficients of variation (CVB) for sperm concentration and morphology.
  • Standardization of results using laboratory-specific Z scores and regression analysis.

Main Results:

  • High between-laboratory variability was observed for sperm morphology (mean CVB 32%) and statistically significant variation for sperm concentration (mean CVB 7%).
  • Standardization using Z scores did not significantly reduce the observed between-laboratory differences for any semen parameter.
  • Analysis of variance confirmed statistically significant differences between laboratories for all tested parameters (P<0.001).

Conclusions:

  • Substantial between-laboratory variability exists for sperm morphology, and significant variation exists for sperm concentration.
  • Standardization methods, including Z scores, are insufficient to eliminate between-laboratory variability in semen analysis.
  • Further strategies are needed to enhance the consistency and reliability of semen analysis across different laboratories.