Behavioral Insights: most popular articles 2019 🎁
Behavioral Insights.
We are approaching the end of 2019. To wrap up, I have made a list of the 10 most popular articles from this year's editions of this newsletter. Enjoy!
Looking for new articles? A short recap of what I’ve been reading this week is on my blog.
Most popular articles 2019
1. Explaining p-values
Everyone running experiments should understand concepts like p-values. Cassie Kozyrkov, Chief Decision Scientist at Google, has made three very short Youtube video's explaining p-values and statistical significance. View explanations.
2. How data literate are you?
Get your personalized data literacy assessment and customized training recommendations via Databilities. Get your free assessment.
3. List of Conversion Rate Optimization tools
Kudos to Simon Vreeman for making this list of CRO tools. Check it out.
4. Measuring the Impact of Online Personalisation
The authors of this paper take perspectives from different areas and propose a research roadmap for future research. Read the article.
5. Checklists for trustworthy analysis of experiments
Another interesting paper on the value of checklists in setting up and analyzing results from online controlled experiments. Read the article.
6. Seeing statistics unfold
Daniel Kunin made a website to make statistics more accessible through interactive D3.js visualizations. Not only beautiful but also very functional. See it for yourself.
7. Example bol.com A/B test
GoodUI found an A/B test on the bol.com website. What can you learn from it? Read the article.
8. We need Data Samurai
Brent Dykes thinks we need a new breed of Data Analysts: the Data Samurai. They are data explorers, coaches, storytellers, stewards and have data science skills. Read the article.
9. Should you be a generalist or a specialist?
Be both, be a T-Shaped Data Professional. Read the article.
10. Synthetic control and alternatives to A/B testing at Uber
Recording of a presentation Nick Jones and Sam Barrows from Uber did at PyData in Amsterdam. Good explanation of why Uber sometimes cannot use traditional A/B testing and what they do to still be able to make causal inferences. Watch the presentation (22 mins).
*|INTERESTED:Would you also like insights in Dutch?:Yes please / ja graag|*Job opportunities in The Netherlands:
Team Lead Business Intelligence at Coolblue (Rotterdam)
Sr. Marketing Analist at Eneco (Rotterdam)
Online Conversion Specialist at de Bijenkort (Amsterdam)
Jr. Web Analytics Specialist at bol.com (Utrecht)*|END:INTERESTED|*
Wishing you a happy, healthy and data-driven 2020. See you next year!
— Kevin
ps. today is my birthday. If you find this newsletter of value, it would mean the world to me if you would share it with a friend or colleague. You can point people to kevinanderson.nl/insights. Thanks!