Wolfgang Forstmeier
Biographic Data
| ID | 2777871 |
|---|---|
| NAME | Wolfgang Forstmeier |
| GIVEN NAMES | Wolfgang |
| FAMILY NAME | Forstmeier |
| SIGNATURE | FORSTMEIER W |
| AFFILIATIONS | Max Planck Society |
| ORCID | 0000-0002-5984-8925 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 11 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 1 |
Violating the normality assumption may be the lesser of two evils
When data are not normally distributed, researchers are often uncertain whether it is legitimate to use tests that assume Gaussian errors, or whether one has to either model a more specific error structure or use randomization techniques. Here we use Monte Carlo simulations to explore the pros and cons of fitting Gaussian models to non-normal data in terms of risk of type I error, power and utility for parameter estimation. We find that Gaussian …
Cryptic multiple hypotheses testing in linear models
Fitting generalised linear models (GLMs) with more than one predictor has become the standard method of analysis in evolutionary and behavioural research. Often, GLMs are used for exploratory data analysis, where one starts with a complex full model including interaction terms and then simplifies by removing non-significant terms. While this approach can be useful, it is problematic if significant effects are interpreted as if they arose from a s…
Conclusions beyond support
Mixed-effect models are frequently used to control for the nonindependence of data points, for example, when repeated measures from the same individuals are available. The aim of these models is often to estimate fixed effects and to test their significance. This is usually done by including random intercepts, that is, intercepts that are allowed to vary between individuals. The widespread belief is that this controls for all types of pseudorepli…
Superstition and belief as inevitable by-products of an adaptive learning strategy
Superstition and belief as inevitable by-products of an adaptive learning strategy
Conclusions beyond support
Mixed-effect models are frequently used to control for the nonindependence of data points, for example, when repeated measures from the same individuals are available. The aim of these models is often to estimate fixed effects and to test their significance. This is usually done by including random intercepts, that is, intercepts that are allowed to vary between individuals. The widespread belief is that this controls for all types of pseudorepli…
Cryptic multiple hypotheses testing in linear models
Fitting generalised linear models (GLMs) with more than one predictor has become the standard method of analysis in evolutionary and behavioural research. Often, GLMs are used for exploratory data analysis, where one starts with a complex full model including interaction terms and then simplifies by removing non-significant terms. While this approach can be useful, it is problematic if significant effects are interpreted as if they arose from a s…
Violating the normality assumption may be the lesser of two evils
When data are not normally distributed, researchers are often uncertain whether it is legitimate to use tests that assume Gaussian errors, or whether one has to either model a more specific error structure or use randomization techniques. Here we use Monte Carlo simulations to explore the pros and cons of fitting Gaussian models to non-normal data in terms of risk of type I error, power and utility for parameter estimation. We find that Gaussian …
Mathematics (4 works) · Statistics (4 works) · Econometrics (3 works) · Type I and type II errors (3 works) · Artificial Intelligence (2 works) · Biology (2 works) · Computer Science (2 works) · Contrast (vision) (2 works) · Null hypothesis (2 works) · Sample size determination (2 works)