Let's work together on your project ...

Design & Sampling

  • Critically evaluating your study aims and hypotheses (are they logical, testable?).
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  • Determining a robust study design to collect your data, so it's free from bias, pseudo-replication, and other threats that could jeopardize its validity and publication.*
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  • Helping you decide upon a sample size that has enough power but is logistically feasible, too.
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  • Guiding you on how to develop and appraise your research methodology and prioritize which data to collect.
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  • Collaborating on a pilot study, its analyses, and what it means for your research plan and its implementation.*​

Data Analysis

  • Resolving problems in your statistical analyses ("trouble-shooting"), or dataset.
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  • Warning you of common statistical pitfalls in data analysis.
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  • Selecting the right statistical methods to apply to your data, given the experimental design.* 
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  • Performing statistical analyses and providing their outputs and diagnostics, with some commentary provided.*
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  • Helping you understand what the statistical analyses can (and cannot) tell you about your data and hypotheses.
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  • Advising you on how to present your results, both in writing and in your Tables and/or Figures.​

Statistical Review

  • Carefully editing the English used to accurately describe your design, sampling, and data analyses in your Methods section of your manuscript, report, or chapter.
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  • Checking your document for any misreporting or misinterpretation in the statistical methods applied. 
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  • Editing to ensure the statistical notation/content is consistent, free of mistakes,  clear, understandable, and transparent.
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  • Ensure the appropriate use of the word 'significant' throughout your text.
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  • Reviewing and commenting on all your Figures or Tables for their applied statistics.
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  • Pointing out fallacies in statistical inference, questionable reasoning, or unjustified generalizations.
*For students, guidance or coaching offered only.

Correctly applying and reporting your statistical analyses, along with careful English editing, will improve the likelihood your research paper is accepted while minimizing the time spent in revision.

For post-docs:  Advice and coaching on research methodology, using proper experimental replication and controls, appropriately fitting statistical models for hypothesis testing, improving figures and tables, and ensuring statistics are correctly used, interpreted, and reported in your manuscripts before submitting them for publication.
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For doctoral students:  Help with hypothesis formulation and experimental design, data exploration and preparation, data analysis and interpretation, transparent statistical reporting, and communicating your results effectively. 

For students:  Help with completing or checking of your course work, and tutoring in basic and advanced statistical concepts and methods.

Aspects and techniques of applied statistics currently available*: 
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Solving problems in statistical analyses
Spatial/temporal pseudo-replication
Experimental design & randomization
Stratified & repeated sampling
Data exploration & variable transformations
Test assumptions, model diagnostics
Parametric and non-parametric tests
Comples split-plot designs
ANOVA family (ANCOVA, MANOVA)
Multiple & logistic regression
Generalized linear models
Mixed models (multilevel, nested data) 
Multivariate approaches
Frequency and survival analyses
Likelihood methods
Analysis of 'messy' data

*Bayesian approaches for statistical inference are not supported at this time.