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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

3.8K
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...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Contaminants and Errors01:16

Contaminants and Errors

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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Margin of Error01:27

Margin of Error

4.5K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Sampling Soils in a Heterogeneous Research Plot
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Sample size justification in feasibility studies: moving beyond published guidance.

Robert Montgomery1

  • 1Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, 66160, KS, USA. rmontgomery@kumc.edu.

Pilot and Feasibility Studies
|June 23, 2025
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Summary

Pilot and feasibility studies need better sample size justifications. Basing them on operating characteristics ensures reliable decisions for future clinical trials.

Keywords:
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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Research Design

Background:

  • Pilot and feasibility studies are essential for informing decisions about subsequent, larger randomized controlled trials.
  • Current sample size justifications for feasibility studies often rely on pragmatic reasons or rules of thumb, lacking statistical rigor.
  • There is a need for clearer best practices in sample size determination for these preliminary studies.

Discussion:

  • Feasibility studies should be designed to report key operating characteristics, such as the probability of correctly identifying a feasible future trial.
  • Sample size justifications should ideally be based on these operating characteristics to enhance decision-making reliability.
  • Inadequate reporting of operating characteristics hinders the interpretation of feasibility study results and their utility for future trial planning.

Key Insights:

  • Sample size in feasibility studies is critical for accurate decision-making regarding future trials.
  • Operating characteristics offer a robust framework for justifying sample sizes in pilot and feasibility research.
  • Improved reporting standards are necessary to increase the value and impact of feasibility studies.

Outlook:

  • Developing standardized methods for sample size justification in feasibility studies is crucial.
  • Future research should focus on implementing and evaluating sample size strategies based on operating characteristics.
  • Enhanced reporting of feasibility studies will improve the efficiency and success rate of clinical trial development.