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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...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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...
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.
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...

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Model error in covariance structure models: Some implications for power and Type I error.

Donna L Coffman1

  • 1The Pennsylvania State University.

Methodology : European Journal of Research Methods for the Behavioral & Social Sciences
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study found that violating the parameter drift assumption does not significantly impact the Type I error rate for close fit tests or power analyses. The Root Mean Square Error of Approximation (RMSEA) tests remain reliable even when this assumption is breached.

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Area of Science:

  • Psychometrics
  • Statistical modeling

Background:

  • The parameter drift assumption is crucial for statistical power analyses in structural equation modeling.
  • Violations of this assumption may affect the accuracy of fit indices like the Root Mean Square Error of Approximation (RMSEA).

Purpose of the Study:

  • To investigate the impact of violating the parameter drift assumption on Type I error rates and power analyses.
  • To evaluate the robustness of MacCallum, Browne, and Sugawara's (1996) close fit and exact fit tests under assumption violations.

Main Methods:

  • Introduced model error using Cudeck and Browne's (1992) procedure.
  • Compared empirical power with theoretical power for RMSEA tests (≤ .05 for close fit, = 0 for exact fit).
  • Assessed Type I error rates under parameter drift assumption violations.

Main Results:

  • Empirical and theoretical power were nearly identical for both close fit and exact fit tests, even with assumption violations.
  • The test of close fit maintained its nominal Type I error rate despite violations of the parameter drift assumption.

Conclusions:

  • The parameter drift assumption is not critical for the Type I error rate of the close fit test.
  • MacCallum et al.'s (1996) procedures for testing model fit demonstrate robustness to parameter drift assumption violations.