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Related Concept Videos

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Related Experiment Video

Updated: May 22, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

A resampling-based approach to multiple testing with uncertainty in phase.

Andrea S Foulkes1, Victor G DeGruttola

  • 1University of Massachusetts, MA, USA.

The International Journal of Biostatistics
|May 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to analyze genetic data for complex diseases, improving the detection of gene associations by accounting for unobserved genetic phase and adjusting for multiple tests. The approach was successfully applied to identify genetic factors influencing lipid profiles in HIV-1 patients.

Related Experiment Videos

Last Updated: May 22, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Area of Science:

  • Genetics
  • Statistical Genetics
  • Complex Disease Research

Background:

  • Complex diseases require analyzing numerous genetic markers and considering allele phase, which is often unobserved in population studies.
  • Unobserved phase and multiple testing pose significant analytical challenges in genetic association studies.

Purpose of the Study:

  • To develop and validate a statistical method that handles missing genetic phase information and adjusts for multiple testing.
  • To identify potential genetic contributions to lipid profile abnormalities in HIV-1 infected individuals.

Main Methods:

  • A likelihood-based approach was used to manage missing phase data.
  • A resampling method was employed for multiple testing adjustment.
  • The method was applied to a cohort of 626 HIV-1 patients undergoing highly active anti-retroviral therapies.

Main Results:

  • Simulations demonstrated the preservation of the family-wise error rate.
  • The method showed reasonable power for detecting genetic associations.
  • Haplotype effects of hepatic lipase (HL) and endothelial lipase (EL) on high-density lipoprotein cholesterol (HDL-C) were investigated.

Conclusions:

  • The combined statistical approach effectively addresses challenges in genetic association studies.
  • The method is suitable for analyzing genetic contributions to complex traits like lipid abnormalities in patient cohorts.