'Significant’ can be considered one of those not so intuitive terms in statistics. 'A significant effect' has the connotation of a meaningful effect while it only ensures you that some specified method of saying ‘yes’ or ‘no’ to your data is telling you ‘yes’. ‘Yes’ to an effect that might be the opposite of what you want it to be, ‘yes’ to an effect that might be so small that it is of no concern to your field and always a ‘yes’ that might be an improper ‘yes’.
Sometimes drawing conclusions from data is straightforward. But at other times, analytical methods are difficult and mistakes are easily made.
Significant Help's aim is to convey a sense of data by giving advice and customized training. Not only to yield the best results. But also, to give institutions and companies a notion of when statistics is easy, and when it is not. A distinction in all stages of the data process, designing experiments, the selection of modeling approaches and the actual analysis.
Significant is not always the same as important. In my work as a statistical consultant I want to help businesses and institutions to decide what to extract from data that is important to them. In my PhD research, I investigate what the behavior is of statistical methods under imperfect circumstances. I also research alternatives to the most well-known p-value based methods, in light of the current, replication crisis in science*.
* This article sums up the 'crisis' quite well:
graduated Cum Laude in the Master's Program Statistical Science for the Life and Behavioural Sciences, part of Leiden University's Master of Mathematics. Previously, she studied the Bachelor's program Artificial Intelligence at Utrecht University, which she completed with Cum Laude distinction as well. Both research areas contributed to her interest in abstract systems to support human dicision making.
At the moment she spends 4 days a week doing PhD research on Safe Statistics at Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands, under supervision of Prof. dr. Peter Grünwald, and 1 day a week on statistical consulting on behalf of her business Significant Help.
For an overview of past projects, go to Projects.
T-test - Linear Regression - ANOVA - Logistic Regression - Generalized Linear Models - Robust Regression - Delta Method - Mixed Models - Longitudinal Modeling - Generalized Estimating Equations (GEE) - Bayesian Statistics - Discriminant Analysis - Optimal Scaling - Correspondence Analysis - Principal Component Analysis - Monte Carlo Statistics - Bootstrapping - Recommender System - Bayesian networks - K-means - Support Vector Machines - Multiple Testing Correction - High Dimensional Data Analysis - Ridge/Lasso - Splines - Cross-Validation - Random Forests - Bias/Variance Trade-off - Zero-Inflated Poisson Regression - Spatial/Temporal dependence - Finite Population Correction - Power Analysis - Randomized Incomplete Block Design - Survival Analysis - Censoring - Structural Equation Models - Item Response Theory - Imputation - EM Algorithm - Finite Mixture Models - Calibration Problem in Regression - R - SPSS - Weighted Contrasts - Stratified Design - Questionaire - Permutation Tests - Fixed/Common-Effect Meta-Analysis - Random Effect Meta-Analysis - Sequential Analysis - Bayes Factors - Bayesian T-test - Optional Stopping - Sequential Analysis - Meta-Analysis - Cummulative Meta-Analysis - Test Martingales - Uniformly Most Powerful Tests - Safe Tests