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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Random Error01:04

Random 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...
Randomized Experiments01:13

Randomized 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
Simple...
Random Sampling Method01:09

Random Sampling Method

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...
Random and Systematic Errors01:20

Random and Systematic Errors

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...
Random and Systematic Errors01:20

Random and Systematic Errors

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: May 23, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

On random sample size, ignorability, ancillarity, completeness, separability, and degeneracy: sequential trials,

Geert Molenberghs1, Michael G Kenward, Marc Aerts

  • 11I-BioStat, Universiteit Hasselt, Belgium.

Statistical Methods in Medical Research
|April 20, 2012
PubMed
Summary
This summary is machine-generated.

When sample size is random, standard statistical properties like consistency may fail. This impacts estimation in sequential trials, missing data, and random cluster sizes, requiring new frameworks for accurate analysis.

Keywords:
Frequentist inferencegeneralized sample averageinformative cluster sizejoint modelinglikelihood inferencemissing at randomrandom cluster size

Related Experiment Videos

Last Updated: May 23, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

Area of Science:

  • Statistics
  • Statistical Inference

Background:

  • Frequentist statistical properties typically assume a fixed sample size.
  • Random sample sizes, dependent on collected data, present unique challenges in statistical analysis.
  • Existing literature addresses specific cases like sequential trials and missing data.

Purpose of the Study:

  • To develop a unified framework for analyzing statistical properties under random sample sizes.
  • To derive generic results applicable to diverse settings with data-dependent sample sizes.
  • To provide insights and practical consequences for statistical estimation in these scenarios.

Main Methods:

  • Joint modeling of data outcomes and the sampling process.
  • Derivation of generic statistical results applicable to random sample size schemes.
  • Focus on the simplest group sequential setting to illustrate core findings.

Main Results:

  • Standard properties like consistency and optimality may not hold for sample averages with random sample sizes.
  • Small sample bias can occur, and unbiased estimators may not be optimal.
  • Non-ancillarity of sample size and incomplete sufficient statistics contribute to these counterintuitive results.

Conclusions:

  • The study highlights the need for specialized statistical approaches when sample sizes are random.
  • Findings have direct implications for group sequential trials, random cluster sizes, and missing data.
  • A unified framework offers a path to better understanding and addressing these complex statistical challenges.