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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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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.
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Margin of Error01:27

Margin of Error

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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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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.
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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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...
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A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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Sample size calculation with simple math for clinical researchers.

Kameshwar Prasad1

  • 1Department of Neurology, Neurosciences Centre, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India.

The National Medical Journal of India
|August 3, 2021
PubMed
Summary

A simple formula helps researchers quickly estimate sample size for clinical trials without needing complex tools. This method aids in assessing research feasibility, especially in resource-limited settings.

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Area of Science:

  • Clinical Research
  • Biostatistics
  • Health Sciences

Background:

  • Assessing research feasibility requires accurate sample size estimation.
  • Online sample size calculators are often inaccessible in developing countries.
  • Clinicians need a simple method for sample size calculation.

Purpose of the Study:

  • To present a memorable and easy-to-use formula for sample size calculation in controlled clinical trials.
  • To provide a tool for rapid feasibility assessment of research questions.

Main Methods:

  • The study introduces a simplified formula for sample size calculation.
  • Formula for dichotomous outcomes: n = (16p[100-p])/d² per group.
  • Formula for continuous outcomes: n = 16s²/d² per group.

Main Results:

  • The proposed formula allows for mental arithmetic or basic calculator use.
  • It provides a quick estimation for sample size in controlled trials.
  • Modifications for unequal group sizes are mentioned.

Conclusions:

  • The simplified formula enhances the feasibility assessment of clinical research questions.
  • It empowers clinicians and researchers, particularly in resource-limited environments.
  • This method supports practical application of research planning.