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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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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. Among the various sampling methods used by...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Related Experiment Video

Updated: Nov 18, 2025

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
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RANDOMIZE: A Web Server for Data Randomization.

Agaz H Wani1, Don Armstrong2, Jan Dahrendorff1

  • 1Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA.

Archives of Proteomics and Bioinformatics
|February 8, 2021
PubMed
Summary
This summary is machine-generated.

Batch effects in epigenome-wide DNA methylation profiling can skew results. We developed RANDOMIZE, a free web tool to efficiently randomize samples, minimizing bias and improving data accuracy for microarray studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Epigenetics

Background:

  • The Illumina Infinium MethylationEpic BeadChip (Epic 850k) is a standard for epigenome-wide DNA methylation profiling.
  • Batch effects, arising from sample handling, can introduce bias and reduce statistical power in methylation data.
  • Proper study design, including sample randomization, is critical for mitigating technical artifacts.

Purpose of the Study:

  • To address the challenge of efficiently randomizing samples for DNA methylation microarray studies.
  • To develop a user-friendly tool for distributing samples evenly across potential batch effect factors.

Main Methods:

  • Development of RANDOMIZE, a web-based application for sample randomization.
  • The tool distributes samples across factors like rows, chips, and plates to minimize batch effects.
  • Demonstration of the tool's efficiency, speed, and ease of use.

Main Results:

  • The RANDOMIZE tool effectively performs sample randomization for DNA methylation microarray data.
  • The application is efficient, fast, and user-friendly.
  • The tool is freely accessible online with sample data and tutorials.

Conclusions:

  • RANDOMIZE provides a valuable solution for researchers conducting epigenome-wide DNA methylation studies.
  • Effective randomization using this tool can improve the reliability and accuracy of microarray data analysis.
  • The web-based nature ensures accessibility for a wide range of users in the scientific community.