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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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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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One-Way ANOVA: Unequal Sample Sizes01:15

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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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Kendall's Tau Test01:16

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Kendall's tau test, also known as the Kendall rank coefficient test, is a nonparametric method for assessing association between two variables. This test is particularly useful for identifying significant correlations when the distributions of the sample and population are unknown. Developed in 1938 by the British statistician Sir Maurice George Kendall, the tau coefficient (denoted as τ) serves as a rank correlation coefficient, with values ranging from -1 to +1.
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Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

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Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
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Critical Region, Critical Values and Significance Level01:16

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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
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Related Experiment Video

Updated: Apr 26, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

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Nomogram for sample size calculation on a straightforward basis for the kappa statistic.

Hyunsook Hong1, Yunhee Choi2, Seokyung Hahn3

  • 1Division of Medical Statistics, Medical Research Collaborating Center, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea; Department of Biostatistics, Seoul National University School of Public Health, Seoul, Korea.

Annals of Epidemiology
|August 5, 2014
PubMed
Summary

Sample size calculations for interobserver agreement can be simplified. New formulas and nomograms use simple proportion of agreement, avoiding the kappa paradox for more straightforward planning.

Keywords:
Kappa statisticNomogramProportion of agreementSample size

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Research Methodology

Background:

  • Kappa statistic is a standard measure for interobserver agreement.
  • The kappa paradox (high agreement, low kappa value) complicates sample size calculations.
  • Alternative methods are needed for robust sample size estimation in specific scenarios.

Purpose of the Study:

  • To propose new sample size formulae and nomograms for interobserver agreement.
  • To utilize a simple proportion of agreement instead of the kappa statistic.
  • To address limitations of kappa in sample size determination, particularly concerning the kappa paradox.

Main Methods:

  • Derived sample size formulae based on the kappa statistic and goodness-of-fit.
  • Developed nomograms using SAS 9.3 for practical application of the formulae.
  • Focused on scenarios with specific marginal prevalences.

Main Results:

  • Successfully produced sample size formulae using a simple proportion of agreement.
  • Developed nomograms to simplify the application of these formulae, overcoming mathematical complexities.
  • Provided tools to facilitate sample size planning in interobserver agreement studies.

Conclusions:

  • The proposed nomograms for sample size calculation using simple proportion of agreement are valuable.
  • These tools are particularly useful in the planning stages of studies involving two raters and nominal outcomes.
  • The approach offers a more practical alternative to kappa for hypothesis testing of interobserver agreement.