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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
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  3. Reference Intervals Revisited: A Novel Model For Population-based Reference Intervals, Using A Small Sample Size And Biological Variation Data
  1. Home
  3. Reference Intervals Revisited: A Novel Model For Population-based Reference Intervals, Using A Small Sample Size And Biological Variation Data

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Reference Intervals Revisited: A Novel Model for Population-Based Reference Intervals, Using a Small Sample Size and

Abdurrahman Coşkun1,2, Sverre Sandberg3,4,5, Ibrahim Unsal1

  • 1Acibadem Labmed Clinical Laboratories, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.

Clinical Chemistry
|August 26, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

A new biological variation (BV)-based model for population-based reference intervals (popRIs) provides robust results using fewer individuals. This approach offers a more efficient method for establishing clinical laboratory reference intervals.

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Area of Science:

  • Clinical Chemistry
  • Hematology
  • Biostatistics

Background:

  • Conventional population-based reference intervals (popRIs) rely on large sample sizes (≥120 individuals).
  • Establishing accurate popRIs is crucial for interpreting laboratory test results.

Purpose of the Study:

  • To explore a novel model for popRIs utilizing biological variation (BV) data.
  • To compare BV-based popRIs with conventional popRIs using varying sample sizes.

Main Methods:

  • A BV-based model was developed using population set point (PSP) and total variation (BV + analytical variation).
  • BV-based popRIs were calculated for 48 measurands using sample sizes of n=16, n=30, and n=120.
  • Simulation studies estimated the minimum number of reference individuals required.

Main Results:

  • The median ratio of BV-based to conventional RI ranges was 0.98.
  • BV-based popRIs were similar across different sample sizes and generally met the 90% coverage criterion.
  • Fewer individuals were needed to estimate the PSP for most measurands compared to conventional methods.

Conclusions:

  • The BV-based popRI model yields robust reference intervals for most measurands.
  • This model requires a smaller reference group than conventional popRIs.
  • Implementation is feasible with reliable biological variation data.