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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: 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...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
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.
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...

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Related Experiment Video

Updated: May 30, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

Using interpolation to estimate system uncertainty in gene expression experiments.

Lee J Falin1, Brett M Tyler

  • 1Virginia Bioinformatics Institute, Virginia Polytechnic and State University, Blacksburg, Virginia, United States of America.

Plos One
|July 30, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm to quantify uncertainty in biological data gaps. It guides future experiments by identifying the most informative samples to collect, optimizing expensive high-throughput assays.

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Area of Science:

  • Systems Biology
  • Genomics
  • Computational Biology

Background:

  • High-throughput assays generate vast biological data but often have sparse samples due to high costs.
  • Limited sampling restricts understanding of dynamic biological processes and treatment effects.
  • Existing datasets frequently lack sufficient replicates or time points for robust analysis.

Purpose of the Study:

  • To develop a novel algorithm for quantifying uncertainty in unmeasured intervals of quantitative biological data.
  • To guide experimental design by identifying data points that would maximize information gain.
  • To enhance the utility of sparse, high-cost biological datasets.

Main Methods:

  • Developed a novel algorithm to estimate probabilistic distributions of gene expression values in unmeasured intervals.
  • Utilized plausible biological constraints to model data within these intervals.
  • Applied the algorithm to quantitative systems biology measurements, including time-series gene expression data.

Main Results:

  • The algorithm quantifies uncertainty in unmeasured biological data intervals.
  • It successfully identifies which future samples would provide the most valuable information.
  • Demonstrated applicability to gene expression time-series and other quantitative treatments.

Conclusions:

  • Quantifying uncertainty in biological data gaps can significantly improve experimental efficiency.
  • The method aids in optimizing data collection strategies for high-throughput systems biology studies.
  • This approach can simplify exploration of complex treatment spaces and reduce experimental costs.