Related Experiment Video
Updated: May 10, 2026
07:11
Sampling Soils in a Heterogeneous Research Plot
Published on: January 7, 2019
Reference interval studies: what is the maximum number of samples recommended?
Clinical Chemistry and Laboratory Medicine
|June 27, 2013
Summary
Determining the maximum number of specimens for reference interval calculation is crucial. This study presents a model to optimize sample sizes, reducing costs by identifying the point of diminishing returns for reference interval studies.
Area of Science:
- Clinical Chemistry
- Laboratory Medicine
- Biostatistics
Background:
- Reference interval determination is essential for clinical diagnostics.
- Optimal specimen number for reference interval calculation remains underexplored.
- Current methods may involve excessive or insufficient sample sizes.
Purpose of the Study:
- To develop and present a model for estimating the maximum number of specimens needed for reference interval studies.
- To establish a method for determining the point beyond which additional specimens offer no significant benefit.
- To optimize resource allocation in laboratory medicine.
Main Methods:
- Derived equations for confidence interval bounds of reference limits.
- Applied derived equations to transformed and untransformed data.
- Calculated maximum specimen numbers for 12 common analytes.
Main Results:
- Maximum sample sizes varied widely (126 to 18,171 specimens).
- Required sample size depends on population standard deviation and data transformation.
- The chosen analyte reporting interval significantly influences the maximum number of specimens.
Conclusions:
- The reporting interval critically impacts reference interval calculations.
- This model-based approach can significantly reduce the cost of reference interval studies.
- Optimizing specimen numbers enhances efficiency in laboratory diagnostics.
Related Concept Videos
01:16Contaminants and Errors
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
01:17Systematic Sampling Method
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
Systematic sampling is one of the simplest methods...
01:19Interpretation 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 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...
01:19Sample Size Calculation
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
01:13Range Rule of Thumb to Interpret Standard Deviation
The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height of...
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height of...
01:21Confidence 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...
A confidence...