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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.
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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Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Sample Size Calculations for Comparing Groups with Continuous Outcomes.

Julia Z Zheng1, Yangyi Li2, Tuo Lin3

  • 1Department of Immunology and Microbiology, McGill University, Montreal, Canada.

Shanghai Archives of Psychiatry
|September 29, 2017
PubMed
Summary
This summary is machine-generated.

Justifying sample size is crucial for clinical studies. Power and sample size analysis is an interactive process where researchers and statisticians collaborate as equal partners.

Keywords:
clinical studycontinuous outcomepowersample size

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Area of Science:

  • Biomedical Research
  • Clinical Trials
  • Statistical Analysis

Background:

  • Sample size justification is a mandatory component of all clinical study protocols.
  • Many researchers perceive power and sample size analysis as a complex statistical procedure.
  • This perception can create a barrier to effective research design and execution.

Purpose of the Study:

  • To demystify power and sample size calculations for biomedical and clinical researchers.
  • To emphasize the critical role of clinical investigators in the sample size determination process.
  • To highlight the collaborative nature of power and sample size analysis.

Main Methods:

  • Discussion of the principles and methodologies behind power and sample size calculations.
  • Illustrative examples of how investigator input enhances the meaningfulness of the analysis.
  • Emphasis on the interactive dialogue between researchers and statisticians.

Main Results:

  • Power and sample size analysis is not solely a statistician's task.
  • Biomedical and clinical investigators significantly contribute to the feasibility and relevance of these analyses.
  • The process is most effective when approached as a partnership.

Conclusions:

  • Power and sample size analysis is an interactive and collaborative endeavor.
  • Effective research requires statisticians and clinical investigators to function as equal partners.
  • This collaborative approach ensures scientifically rigorous and meaningful study designs.