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Related Concept Videos

Variance01:15

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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
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Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
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Negative values and variance functions: Implications for statistical analysis.

William A Sadler1

  • 1Retired, formerly at: Nuclear Medicine Department, Christchurch Hospital, Christchurch, New Zealand.

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|December 18, 2020
PubMed
Summary
This summary is machine-generated.

Suppressing negative results in biological specimen analysis, while clinically sensible, can distort statistical findings. This study advocates for retaining negative results to improve the accuracy of statistical analyses, particularly for low-end uncertainty estimates.

Keywords:
Negative resultsbiasdata censoringstatistical analysisvariance functions

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Area of Science:

  • Clinical Chemistry
  • Biostatistics
  • Laboratory Medicine

Background:

  • Current practice involves suppressing negative concentration results in biological specimens, often by left-censoring to zero or other detection limits.
  • This suppression, while clinically practical for reporting, is an artifact of data reduction and can obscure true data variability.

Purpose of the Study:

  • To highlight the adverse consequences of suppressing negative results on statistical analyses, especially parametric methods.
  • To advocate for the availability of negative results in data reporting.
  • To discuss challenges in variance function estimation and propose practical solutions.

Main Methods:

  • Conceptual analysis of data reporting practices in biological specimen analysis.
  • Discussion of statistical implications, focusing on parametric summaries and uncertainty estimation.
  • Exploration of complications in estimating variance functions with censored data.

Main Results:

  • Suppressing negative results, though clinically useful, can lead to unreliable estimates of low-end uncertainty.
  • Parametric statistical summaries and analyses are particularly vulnerable to biases introduced by data suppression.
  • Estimating variance functions becomes complicated when negative results are systematically removed or altered.

Conclusions:

  • Retaining negative results, despite being non-sensical as concentrations, is crucial for accurate statistical analysis.
  • Clinicians and researchers should be aware of the statistical trade-offs when censoring negative data.
  • Practical workarounds are needed to manage and analyze data that includes available negative results.