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Related Concept Videos

Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
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...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...

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Related Experiment Video

Updated: Jun 20, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Using a nonparametric bootstrap to obtain a confidence interval for Pearson's r with cluster randomized data: a case

David A Wagstaff1, Elvira Elek, Stephen Kulis

  • 1College of Health and Human Development, The Pennsylvania State University, 153 Henderson Building, University Park, PA 16802, USA. daw22@psu.edu

The Journal of Primary Prevention
|August 18, 2009
PubMed
Summary

Fifth-grade students

Related Experiment Videos

Last Updated: Jun 20, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Public Health
  • Substance Abuse Prevention
  • Statistical Methods

Background:

  • Substance use prevention programs in schools aim to reduce adolescent substance use.
  • Understanding the factors influencing adolescents' intentions regarding substance use is crucial for effective prevention.
  • Cluster randomized trials are common in school-based prevention research.

Purpose of the Study:

  • To estimate the association between 5th graders' positive substance use expectancies and their intentions to not use substances.
  • To test the null hypothesis of no association between these variables.
  • To illustrate the application of a nonparametric bootstrap method for cluster randomized data.

Main Methods:

  • A nonparametric bootstrap approach was employed to derive interval estimates for Pearson's r.
  • Data were collected from 5th-grade students participating in a school-based substance use prevention program.
  • The unit of randomization was the public middle school, indicating cluster randomized data.

Main Results:

  • Positive substance use expectancies explained 21% of the variability in students' intentions to not use substances.
  • A significant association was found, with Pearson's r = 0.46 (95% Confidence Interval [0.40, 0.50]).
  • The study highlighted the importance of identifying and addressing outliers in cluster randomized data.

Conclusions:

  • Nonparametric bootstrap is a valuable tool for analyzing cluster randomized data in prevention research.
  • Substance use expectancies are a significant predictor of adolescents' intentions regarding substance use.
  • Careful consideration of outliers and cluster sizes is essential for robust findings in school-based prevention studies.