The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review
Abstract
:Simple Summary
Abstract
1. Forensic Entomology, an Inferential Science
2. Issues Associated with Successional Data
2.1. Data Measured Repetitively from a Small Number of Sampling Units and Field Sites
2.2. Non-Linearity
2.3. Datasets with a Relatively Large Proportion of Unexplained Variance
2.4. Data Affected by Temporal and Spatial Effects
2.5. Datasets Including Many Independent and Dependent Variables
3. Possible Remedies to the Issues Associated with Successional Data
3.1. How to Solve Problems Related to Low Statistical Power as well as to Low Internal and External Validity
3.2. How to Solve Problems Related to Interdependence between Records, Auto-Regressive Covariance Structure, Non-Linear Effects and Non-Gaussian Distributions
3.3. How to Solve Problems Related to Autocorrelation, Multicollinearity, Overfitting and Alpha Inflation
3.4. How to Solve Problems Related to Having Data Interrelated in Time or Space
3.5. How to Solve Problems Related to Having a Large Amount of Systematic Variance
4. Advice to Scientific Editors, Reviewers and Academic Supervisors
- The study is devoid of experimental errors. Scientific editors and reviewers should not be afraid to require from authors a detailed description and a map of the layout of the study. Regardless of the nature of the study, the experimental unit should always be clearly identified. To learn how to recognize the experimental unit and main experimental errors, read [12‒14,62]. Pseudoreplicated studies should never be published, even as “preliminary studies”.
- If the nature of the study allows for it, an inferential statistical test that permits extrapolation of the results to case scenarios is presented. The statistical procedures should be described in detail and an estimate of the experimental error should be evident in the tables and figures of the manuscript. If successional data are involved, the statistical test should comply with elements presented in Table 2.
- If the nature of the study does not allow for it, no inferential statistical test is presented. In a widely cited article, Hurlbert [62] suggested that good articles that refrain from using inferential statistics when these cannot be applied are worth publishing. However, the authors should explicitly recognize that the study is descriptive, thus not allowing for transposition of the results to other situations or use in court.
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Issues | Consequences |
---|---|
1. Data measured repetitively from a small number of sampling units and field sites | Data interdependence Autoregressive covariance structure Low statistical power Low internal validity |
2. Data presenting non-linear trends | Non-linear effects Overfitting Non-Gaussian distribution |
3. Datasets with a relatively large proportion of unexplained variance | High proportion of systematic variance Low external validity |
4. Data affected by temporal and spatial effects | Data interrelated in time Data interrelated in space |
5. Datasets including many independent and dependent variables | Autocorrelation Multicollinearity Overfitting Alpha inflation |
Consequences of Successional Data | Remedies |
---|---|
1. Low statistical power Low internal validity Low external validity | Increase the sample size Increase the number of locations, times, and conditions |
2. Data interdependence Autoregressive covariance structure Non-linear effects Non-Gaussian distribution | Generalized linear models (GLMs), generalized linear mixed models (GLMMs), generalized additive models (GAMs), generalized additive mixed models (GAMMs) |
3. Autocorrelation Multicollinearity Overfitting Alpha inflation | Multivariate statistics |
4. Data interrelated in time Data interrelated in space | Time series analysis Spatial statistics Repeated measures and/or spatially explicit GLMs, GLMMs, GAMs, GAMMs |
5. High proportion of systematic variance | Ensure that all influential variables have been accounted for Use a model that is better suited to data Acknowledge this variability |
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Moreau, G. The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review. Insects 2021, 12, 240. https://doi.org/10.3390/insects12030240
Moreau G. The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review. Insects. 2021; 12(3):240. https://doi.org/10.3390/insects12030240
Chicago/Turabian StyleMoreau, Gaétan. 2021. "The Pitfalls in the Path of Probabilistic Inference in Forensic Entomology: A Review" Insects 12, no. 3: 240. https://doi.org/10.3390/insects12030240