Mathematics
8 years ago

Unfulfilled Assumptions in Analysis of Variance Lead to Biased Estimates

Biometrics
Some Consequences When the Assumptions for the Analysis of Variance are not Satisfied
W. G. Cochran

Paper Summary

Related papers

Decision Sciences
3 years ago

P Values: The Flawed Measure That Could Change Scientific Discoveries

Evidence Synthesis
2 views
Decision Sciences
5 years ago

Flawed Hypothesis Testing Methods Lead to Misleading Statistical Decisions.

Time Series Forecasting
Multicollinearity
3 views
Decision Sciences
5 years ago

Statistical tricks lead to rejection of null hypothesis in behavioral sciences

Evidence Synthesis
Clinical Trials
Network Analysis
1 view

The article discusses what happens when the assumptions needed for the analysis of variance are not met. The main goals are to estimate differences accurately, determine the accuracy of estimates, and perform tests of significance. When the assumptions are not fulfilled, the estimated variances may become biased, leading to inaccurate results. It is important to detect these failures in assumptions and avoid serious consequences to ensure the validity and reliability of statistical analyses.