Significant Help:     Statistical Consulting and Case-Based Statistical Training

'Significant’ can be considered one of those not so intuitive terms in statistics. 'A significant effect' has the connotation of something meaningful 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 at CWI I develop statistical methods to summarize scientific studies that expand on one another, where dependencies arise between study series size, meta-analysis timing and individual study results. These methods should help scientists to prioritize research, interpret findings and design new studies – so efficiently accumulate scientific knowledge. Our ALL-IN meta-analysis approach is bottom-up, specifically focused on combining studies that have no (top-down) common design.


"Scientists need to make sure that all relevant trials are in when they hedge their bets on future research."


This interview by Academic Positions gives a nice and short summary of my research at CWI.

My research is motivated by the 'replicability crisis' * in empirical science and the Reducing Research Waste movement in biomedical research (http://rewardalliance.net/).


* This article sums up the 'crisis' quite well:

https://www.firstthings.com/article/2016/05/scientific-regress



  Judith ter schure

                    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 decision making.

At the moment she spends four days a week doing PhD research on ALL-IN meta-analysis at Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands, under the supervision of Prof. dr. Peter Grünwald, she governs the Dutch Society for Statistics and Operations Research (VVSOR) as a treasurer and spends one day a week on statistical consulting on behalf of her business Significant Help.

                      

For an overview of past projects, go to Projects.