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Related Concept Videos

Contaminants and Errors01:16

Contaminants and Errors

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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...
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Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
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Critical Values01:31

Critical Values

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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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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Related Experiment Video

Updated: Dec 29, 2025

Development of a Quantitative Recombinase Polymerase Amplification Assay with an Internal Positive Control
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Maximizing confidence in a negative result: Quantitative sample adequacy control.

Ivan Brukner1, Shaun Eintracht2, Andreas I Papadakis3

  • 1Department of Medical Microbiology, Jewish General Hospital, Canada; Lady Davis Institute for Medical Research, Canada; McGill University, Faculty of Medicine, Canada.

Journal of Infection and Public Health
|February 11, 2020
PubMed
Summary
This summary is machine-generated.

Sample Adequacy Control (SAC) is crucial for reliable qPCR testing. Without SAC, false negatives are possible, compromising infection control and diagnostic accuracy in pathogen detection.

Keywords:
ConfidenceInfection controlLaboratoryNasal swabNegative resultNon-competitive SACPandemiaRespiratory infectionSample adequacy controlqPCR

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Area of Science:

  • Molecular Biology
  • Infectious Disease Diagnostics
  • Clinical Laboratory Science

Background:

  • Quantitative PCR (qPCR) is vital for rapid pathogen detection and infection control.
  • Many qPCR assays lack Sample Adequacy Control (SAC), which assesses specimen quality and quantity.
  • Absence of SAC undermines confidence in negative qPCR results and compromises screening efficacy.

Purpose of the Study:

  • To highlight the critical need for Sample Adequacy Control (SAC) in qPCR assays.
  • To explain barriers to the widespread adoption of SAC in routine screening.
  • To propose a protocol and methods for establishing SAC thresholds.

Main Methods:

  • Analysis of cycle threshold (Cq) values for Influenza A positive results.
  • Evaluation of Cq values for SAC from nasopharyngeal swabs.
  • Correlation analysis between SAC signal strength and confidence in negative results.

Main Results:

  • A weak positive SAC signal (late Cq values) does not guarantee confidence in a negative qPCR result.
  • The exclusion of SAC can lead to false-negative outcomes in pathogen detection.
  • Specific analytical problems addressed by SAC include sample adequacy, processing, and assay inhibition.

Conclusions:

  • Sample Adequacy Control (SAC) is an integral and critical laboratory control for qPCR.
  • Widespread inclusion of SAC is essential to prevent false negatives and ensure reliable diagnostic screening.
  • A defined protocol for SAC threshold establishment is necessary for routine implementation.