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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
Reliability and Validity01:29

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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Related Experiment Video

Updated: Jul 19, 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

The Hazards of Predicting Divorce Without Crossvalidation.

Richard E Heyman1, Amy M Smith Slep

  • 1State University of New York at Stony Brook.

Journal of Marriage and the Family
|October 27, 2006
PubMed
Summary

Divorce prediction models are often overly optimistic. Without crossvalidation and considering divorce prevalence, their accuracy significantly decreases, requiring cautious interpretation of initial findings.

Related Experiment Videos

Last Updated: Jul 19, 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:

  • Psychology
  • Sociology
  • Family Studies

Background:

  • Previous divorce prediction studies (e.g., Gottman et al., 1998) reported high accuracy.
  • These studies often overlooked crucial validation steps like crossvalidation.
  • The prevalence of divorce in the general population was not adequately considered.

Purpose of the Study:

  • To re-evaluate the accuracy of divorce prediction models.
  • To assess the impact of crossvalidation on predictive accuracy.
  • To highlight the importance of population prevalence in divorce prediction research.

Main Methods:

  • Analysis of archival data from previous divorce prediction studies.
  • Implementation of crossvalidation techniques on existing prediction equations.
  • Examination of the influence of divorce prevalence rates on model performance.

Main Results:

  • Predictive accuracy and value dropped substantially during crossvalidation.
  • Initial impressive results were not sustained when models were tested on new data.
  • Failure to account for divorce prevalence further inflated perceived accuracy.

Conclusions:

  • Divorce prediction models require rigorous crossvalidation for reliable results.
  • Results from studies lacking crossvalidation should be viewed with significant caution.
  • Accurate divorce prediction necessitates considering both predictive power and population base rates.