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Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
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Updated: Jul 5, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Statistical inference on categorical variables.

Susan M Perkins1

  • 1Division of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA.

Methods in Molecular Biology (Clifton, N.J.)
|May 3, 2008
PubMed
Summary
This summary is machine-generated.

This chapter explains categorical data analysis, covering nominal and ordinal types and their distributions like binomial and multinomial. It details statistical inference methods for common scenarios, including contingency tables and sample size estimation.

Related Experiment Videos

Last Updated: Jul 5, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Statistics
  • Biostatistics

Background:

  • Categorical data represent characteristics, not numerical values, of experimental units.
  • Understanding data types (nominal, ordinal) and distributions (binomial, multinomial) is crucial for analysis.

Purpose of the Study:

  • To describe types and distributions of categorical data.
  • To present methods for statistical estimation and inference for categorical data.

Main Methods:

  • Describing nominal and ordinal data types and binomial, multinomial, and independent multinomial distributions.
  • Presenting estimation and inference methods for one and two binomial samples.
  • Detailing inference for 2 x 2 and R x C contingency tables and sample size estimation.

Main Results:

  • Provides a comprehensive overview of categorical data analysis techniques.
  • Illustrates methods with relevant data examples and study design discussions.

Conclusions:

  • Offers practical guidance for statistical analysis of categorical data in various research contexts.
  • Aims to enhance understanding and application of categorical data inference methods.