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Random Error01:04

Random Error

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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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Random Variables01:09

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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.
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Randomized Experiments01:13

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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.
Simple randomization
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Random and Systematic Errors01:20

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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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Random Sampling Method01:09

Random Sampling Method

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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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Stereoacuity Improvement using Random-Dot Video Games06:25

Stereoacuity Improvement using Random-Dot Video Games

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Presented here is a protocol to improve stereoacuity using gamified perceptual learning software based on random-dot stimuli. Patients are stereo-deficient subjects without strabismus. The protocol combines optometry center visits with home exercises using software. Compliance and stereoacuity evolution are stored in the...
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Related Experiment Video

Updated: Jan 20, 2026

Stereoacuity Improvement using Random-Dot Video Games
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Stereoacuity Improvement using Random-Dot Video Games

Published on: January 14, 2020

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A burn-in(g) question: How long should an initial equal randomization stage be before Bayesian response-adaptive

Edwin Yn Tang1, Stef Baas2, Daniel Kaddaj3

  • 1Department of Statistics, University of Warwick, Coventry, UK.

Statistical Methods in Medical Research
|January 19, 2026
PubMed
Summary
This summary is machine-generated.

Response-adaptive randomization (RAR) trials benefit from a burn-in period, but its optimal length is unclear. This study introduces an exact evaluation method showing burn-in length significantly impacts trial power and accuracy, necessitating careful selection for optimal results.

Keywords:
Conditional exact testbinary outcomesexact operating characteristicstwo-arm trialunconditional exact test

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Bayesian Statistics

Background:

  • Response-adaptive randomization (RAR) enhances participant benefit in clinical trials but complicates statistical analysis.
  • A non-adaptive 'burn-in' period is often used to mitigate these complexities, yet optimal duration guidance is lacking.

Purpose of the Study:

  • To introduce an exact evaluation approach for assessing the impact of burn-in length on statistical operating characteristics in two-arm binary Bayesian RAR (BRAR) designs.
  • To provide guidance on selecting optimal burn-in periods for BRAR trials.

Main Methods:

  • Developed an exact evaluation approach to analyze statistical operating characteristics of BRAR designs with varying burn-in lengths.
  • Investigated the effects of burn-in duration on type I error rates, power, and estimation bias.
  • Utilized exact tests conditioning on total successes and compared them with calibration and asymptotic tests.

Main Results:

  • Common calibration and asymptotic tests exhibit type I error rate inflation in BRAR designs without a burn-in period.
  • Increasing burn-in length reduces but does not eliminate type I error inflation, highlighting the need for exact tests.
  • Exact tests conditioning on total successes demonstrate superior average and minimum power across various burn-in lengths.
  • Burn-in length significantly affects power and participant benefit, with optima often occurring at intermediate lengths.
  • Test statistics influence type I error rates and power; estimation bias varies with treatment effect size and trial size.

Conclusions:

  • The choice of burn-in length is critical for the statistical validity and efficiency of BRAR trials.
  • Exact evaluation methods are essential for accurate assessment of BRAR designs, particularly concerning type I error rates.
  • Optimal burn-in periods are not necessarily the shortest or longest, emphasizing the need for tailored design considerations.