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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 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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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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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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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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Related Experiment Video

Updated: Sep 3, 2025

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology
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From Random Numbers to Random Objects.

Behrouz Zolfaghari1, Khodakhast Bibak2, Takeshi Koshiba3

  • 1Cyber Science Lab, School of Computer Science, University of Guelph, Guelph, ON N1G 2W1, Canada.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

This study introduces a unified method for generating random non-numerical objects, like passwords and CAPTCHAs. It connects random object generation to random number generation, offering solutions for diverse security applications.

Keywords:
Linear Feedback Shift Registers (LFSRs)S-restricted random number generatorinteger compositionsparallel LFSRsrandom number generationrandom object generation

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Area of Science:

  • Cryptography and Computer Science
  • Information Security and Randomness

Background:

  • Security applications require random generation of non-numerical entities like passwords, permutations, and CAPTCHAs.
  • Current methods address each entity type separately, lacking a unified approach.

Purpose of the Study:

  • To propose a unified framework for the random generation of diverse non-numerical entities.
  • To establish random object generation as a general problem solvable by linking it to random number generation.

Main Methods:

  • Formulating random object generation as a distinct problem.
  • Connecting the problem to the established field of random number generation.
  • Developing and analyzing methods to bridge these two areas.

Main Results:

  • A novel, unified approach to generating various random non-numerical entities.
  • Identification and resolution of challenges in linking random object and number generation.
  • Demonstration of the method's efficacy through a random Latin square generation case study.

Conclusions:

  • The proposed unified method offers a generalized solution for random non-numerical entity generation.
  • This approach simplifies and enhances the security of various applications reliant on random data.
  • Future work can extend this framework to other complex random generation tasks.