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Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Distributions to Estimate Population Parameter01:26

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...
Skewness01:06

Skewness

The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency are...
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...

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Updated: Jun 10, 2026

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
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Published on: March 22, 2022

Result Interpretation in Skewed Populations-Are Common Reference Intervals Inadequate?

Dennis J Orton1, Jessica L Gifford2, Gregory A Kline3

  • 1BC Women's and Children's Hospital and Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.

The Journal of Applied Laboratory Medicine
|June 9, 2026
PubMed
Summary
This summary is machine-generated.

Reference intervals (RIs) are crucial for interpreting lab results from healthy populations. Skewed biomarker distributions can lead to misinterpretations, highlighting the need for interpretive aids to improve clinical understanding.

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Published on: June 23, 2012

Area of Science:

  • Clinical Chemistry
  • Biomarker Analysis
  • Laboratory Medicine

Background:

  • Reference intervals (RIs) define the central 95% of healthy population values.
  • Many common biomarkers exhibit skewed, not normal, result distributions.
  • This necessitates careful interpretation of laboratory results.

Purpose of the Study:

  • To demonstrate interpretive variations arising from Gaussian versus skewed reference interval distributions.
  • To evaluate the impact of distribution type on biomarker interpretation.
  • To explore the utility of interpretive aids for skewed data.

Main Methods:

  • Analyzed de-identified patient data for sodium (Gaussian) and total testosterone (skewed).
  • Utilized the refineR package to obtain RIs and Box-Cox parameters.
  • Evaluated results using RI midpoint, median, IQR, z scores, and cumulative percentiles.

Main Results:

  • Sodium RI midpoint and median were similar, reflecting a Gaussian distribution.
  • Total testosterone showed a skewed distribution, with the RI midpoint closer to the 75th percentile.
  • A skewed testosterone result was initially misinterpreted as "borderline low" without z score/percentile context.

Conclusions:

  • Reference intervals aid interpretation against a "disease-free" population.
  • Skewed biomarker distributions can complicate interpretation if clinicians are unaware of distribution characteristics.
  • Interpretive aids, like z scores and cumulative percentiles, can enhance comprehension and prevent misdiagnosis.