EAHIL2015: Introduction to Statistical Thinking

business-163461_1280This session given by Dr Ian Handel from the Roslin Institute in the UK was great fun. Handel asked us what we wanted to cover in the session and wrote them on a whiteboard, crossing them off as we went. We played some fun games with die and chickpeas which funnily enough, didn’t work out the way he wanted it to go first off so we started again – resulting in ‘proper’ results. Tee hee! It was a fast-paced session which might have made some game players count wrongly (me amongst them). So, did I learn anything new? I do have some rudimentary knowledge of statistics but thought I should attend this session because I find I have to have it repeated for me since I don’t use the knowledge often. I tend to forget. Statistics can be dry and for people without a firm mathematical footing, confusing. Handel was very enthusiastic which made the session fun and the really good thing was that he started at the beginning – with mean median and mode. Once you get these down, the rest becomes a little easier to follow. The standard deviation is the range or how spread out your sample is. The null hypothesis (this is something I didn’t get in my rushed introduction to statistics) is the underpinning of determining Type 1 and Type 2 errors and the basis for thinking about p values. A null hypothesis is that there is nothing going on or that there this no effect. It is the opposite of your hypothesis. I found a good video recently that describes type I and type II errors. The p value is set before the study is done and is a % indicating significance. And here is a handy mnemonic – if the p is low, the null must go! Big samples better pin down the differences between groups, but the groups have to be similar – you have to compare like with like. Big samples equal smaller confidence intervals (estimates of population parameters) while smaller samples have larger confidence intervals. We didn’t go into great detail about parameters so I might have to go over these again later on.

Handel discussed how visual depictions of statistics can be misleading, using a bar chart created by some US government department using very large values (something like 100|1000|10000|15000)  on the Y axis. This made the items measured look equal but when you reconfigured the Y axis measurements to a more reasonable configuration (100|500|1000|1500) the equality became seriously inequality. He recommends 3 books: The Tiger That Isn’t: Seeing Through a World of Numbers | The Visual Display of Quantitative Information | Dicing with Death: Chance, Risk and Health. He also recommends 2 websites – one is about funny correlations eg: the number of drownings by falling into pools correlates with the number of films Nicholas Cage starred in and BBC Radio’s statistics program, More or Less.


One response to “EAHIL2015: Introduction to Statistical Thinking

  1. Pingback: Incorporating a research-minded approach to professional practice: opening keynote at EAHIL 2015 #researchminded | Hazel Hall

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