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Related Concept Videos

Confidence Intervals01:21

Confidence Intervals

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 confidence...
Interval Level of Measurement00:55

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between the...
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...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Quartile01:15

Quartile

Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...

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An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
08:59

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment

Published on: December 3, 2020

Reference intervals.

Graham Jones1, Antony Barker

  • 1Department of Chemical Pathology, St Vincent's Hospital, Sydney, Darlinghurst, NSW 2010, Australia.

The Clinical Biochemist. Reviews
|October 15, 2008
PubMed
Summary
This summary is machine-generated.

Establishing a laboratory reference interval requires defining the analyte, method, and pre-analytical factors. This ensures accurate interpretation of patient results for clinical decisions.

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Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Biostatistics

Background:

  • Reference intervals are crucial for interpreting diagnostic test results.
  • Standardized methods for establishing reference intervals are essential for clinical utility.
  • Variability in analytical methods and pre-analytical factors can impact reference interval accuracy.

Purpose of the Study:

  • To outline recommended elements for establishing a robust reference interval.
  • To provide a framework for defining analytes, methods, and data sources.
  • To emphasize considerations for partitioning, statistical measures, and clinical relevance.

Main Methods:

  • Defining the analyte (measurand) and its clinical utility.
  • Specifying the analytical method, accuracy base, and specificity.
  • Detailing pre-analytical considerations, data sources, and statistical measures.
  • Addressing partitioning, rounding, and clinical relevance of limits.

Main Results:

  • A comprehensive process for establishing reference intervals is described.
  • Key elements include analyte definition, method validation, and data source characterization.
  • Considerations for partitioning, statistical analysis, and clinical interpretation are highlighted.

Conclusions:

  • A systematic approach ensures the reliability and clinical applicability of reference intervals.
  • Standardized establishment processes improve diagnostic accuracy and patient care.
  • Careful consideration of all factors leads to more meaningful clinical decisions.