Introduction

Through my ventures into statistics, there have been several books inspiring me on the way. Here, I want to briefly mention three of the most influential (and by me - recommended).

Head First Statistics

Long before I started using statistics regularly, I had two courses in introductory statistics. One of the classes was geared towards engineers and one for biologists. Both were very theoretical and probably gave a solid and very extensive grounding for how to do analysis. Unfortunately, not much of this knowledge stuck with me (I heard from many classmates that it was a shared experience). So when I later encountered things like ‘normal distributions’ and ‘sampling’, I was not entirely sure what it meant.

This book changed that. After discussing it thoroughly with two friends, we finally started getting a grip on the foundations of statistics. I enjoy its silly style and its very visual style.

Link to Head First Statistics

Statistics done wrong

After statistics became more habitual, and I gradually got comfortable with words like ‘ANOVA’ and ’t-test’, there were still many unknowns to step into. Cases where I did things using statistics which felt ‘kind of right’, but with a nagging feeling that I might have missed something.

After reading this book and discussing it thoroughly with one friend, I spotted many issues that I both could have had run into, and in a few cases - that I had run into. This book helped improve my PhD project. It should be mandatory reading for every PhD student doing biostatistics.

Link to Statistics Done Wrong. It’s free!

Introduction to Statistical Learning (with applications in R)

Getting tired of hearing the buzzword ‘machine learning’ gets thrown around as the solution to all data problems? Having a rough idea what it is all about, but still quite fuzzy on how the different methods work, and what the bias/variance trade-off means?

I read this book twice with a friend, and it gave an understanding of what machine learning is all about that I had previously been lacking. It goes to some depth but tries steering clear of getting into too many details (its bigger brother Elements of Statistical Learning is the one you are after if you want more to bite into).

If “applications in R” sound offputting to you - don’t worry. It is fully readable also with no knowledge in R., and if you are Python-literate, some nice people have rewritten considerable parts of the exercises in Python. Here is one example.

Link to Introduction to Statistical Learning. It’s free!

Final words

This list is not an exhaustive collection, but these books have been a valuable part of my learning at one time or another. If you have encountered books that have helped you get into statistics, I would be happy to hear!