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Related Concept Videos

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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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.
William S. Gosset (1876–1937) of the...
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Confidence Interval for Estimating Population Mean01:25

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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...
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Interpretation of Confidence Intervals01:19

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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...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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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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Related Experiment Video

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RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation.

Tatjana Ammer1,2, André Schützenmeister2, Hans-Ulrich Prokosch1

  • 1Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair of Medical Informatics, Erlangen, Germany.

Clinical Chemistry
|October 20, 2022
PubMed
Summary

A new R-package, RIbench, offers a standardized tool for evaluating indirect methods used to estimate reference intervals from real-world data. It demonstrates that modern indirect methods perform comparably or better than direct methods with sufficient sample size and controlled pathological fractions.

Keywords:
data analyticsdata processinglaboratory methods and toolsreference intervalsstatistics

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Area of Science:

  • Clinical laboratory science
  • Biostatistics
  • Medical informatics

Background:

  • Indirect methods estimate reference intervals using real-world data, an active research area with recent method development.
  • A lack of standardized tools hinders the evaluation and comparison of these indirect methods.

Purpose of the Study:

  • To introduce RIbench, a novel benchmarking suite for the quantitative evaluation of indirect methods for reference interval estimation.
  • To provide a standardized platform for comparing existing and new indirect methods.

Main Methods:

  • RIbench utilizes simulated test sets for 10 biomarkers with varying non-pathological distributions (normal, skewed, heavily skewed, skewed-and-shifted).
  • Test sets incorporate diverse sample sizes and pathological distribution characteristics (location, overlap, fraction) to assess method robustness.
  • Performance is evaluated using an overall benchmark score and sub-scores based on z-score deviations from true reference limits.

Main Results:

  • Modern indirect methods showed strong dependence on pathological fraction and sample size.
  • With a pathological fraction up to 20% and a minimum sample size of 5000, most indirect methods achieved results comparable or superior to direct methods.
  • RIbench was used to compare Hoffmann, TML, kosmic, TMC, and refineR methods against a nonparametric direct method.

Conclusions:

  • RIbench is an open-source R-package designed for systematic evaluation of indirect reference interval estimation methods.
  • This tool facilitates the enhancement of indirect methods and improves the accuracy of reference interval estimation.