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Updated: Aug 13, 2026
07:18
Gene-targeted Random Mutagenesis to Select Heterochromatin-destabilizing Proteasome Mutants in Fission Yeast
Published on: May 15, 2018
Randomisation can do many things - but it cannot "fail"
Arthur H Owora1, John Dawson2, Gary Gadbury3
1assistant professor in the Department of Epidemiology and Biostatistics, Indiana University, School of Public Health-Bloomington.
Significance (Oxford, England)
|August 12, 2026
Summary
Randomisation is essential for reliable data insights but is often misunderstood. Addressing these misconceptions is key to improving research accuracy and data interpretation in scientific studies.
Area of Science:
- Statistics
- Research Methodology
Background:
- Randomisation is a cornerstone of robust experimental design.
- Misconceptions surrounding randomisation persist despite its established importance.
Purpose of the Study:
- To identify and clarify common misconceptions about randomisation.
- To emphasize the critical role of proper randomisation in achieving reliable research outcomes.
Main Methods:
- Literature review of existing studies on randomisation.
- Analysis of documented errors and misunderstandings in research applications.
Main Results:
- Several peculiar and troublesome misconceptions regarding randomisation were identified.
- These misunderstandings can compromise the validity of research findings.
Conclusions:
- Clarifying these misconceptions is vital for enhancing the integrity of scientific research.
- Proper understanding and application of randomisation are crucial for generating trustworthy data insights.
Related Concept Videos
01:13Randomized Experiments
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.
Simple randomization
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01:04Random Error
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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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.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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01:20Random and Systematic Errors
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01:20Random and Systematic Errors
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02:01Group Design
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...