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Related Concept Videos

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.
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...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.

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Related Experiment Video

Updated: Jun 4, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner's curse.

Wolfgang Forstmeier, Holger Schielzeth

    Behavioral Ecology and Sociobiology
    |February 8, 2011
    PubMed
    Summary

    Model simplification in generalised linear models (GLMs) inflates false positives, especially with low sample sizes. Presenting full models is recommended to avoid the winner's curse and ensure accurate effect size interpretation.

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    Area of Science:

    • Ecology
    • Evolutionary biology
    • Behavioral ecology

    Background:

    • Generalised linear models (GLMs) are standard in evolutionary and behavioral research.
    • Exploratory data analysis often involves model simplification from complex to simpler models.

    Purpose of the Study:

    • To quantify the impact of model selection on Type I error rates in GLMs.
    • To highlight the issue of cryptic multiple hypothesis testing in evolutionary and behavioral research.

    Main Methods:

    • Simulated data to assess the probability of false positives under different sample size (N) to predictor (k) ratios.
    • Analysis of effect size overestimation and the 'winner's curse' phenomenon.

    Main Results:

    • Model simplification significantly increases the probability of false positives, especially with low N/k ratios.
    • Effect sizes of significant predictors are overestimated, leading to the 'winner's curse' and irreproducible results.
    • Type I error rates can exceed theoretical expectations substantially with model simplification.

    Conclusions:

    • Model selection in GLMs introduces cryptic multiple hypothesis testing, inflating false discovery rates.
    • Presenting full models is favored to accurately reflect the scope of predictors and avoid biased effect sizes.
    • Full model tests and P-value adjustments can serve as benchmarks for sampling variation effects.