I Hate  Statistics – Pim Bellinga, Founder & Thijs Gillebaart, Co-Founder

We are I Hate Statistics, two social entrepreneurs from Amsterdam who like solving problems. We have set ourselves one goal: teaching even those people who are inclined to think they hate statistics how to understand the world through data.  On January 16, we’ll be sharing our experiences and vision on how to help everyone learn to love data and statistics.

Sharing data is not enough

One of the organisations that has greatly inspired us in our goal and has paved our way to join the UN World Data Forum in Cape Town this year is the Swedish fact tank Gapminder. The global data heroes from Sweden are doing an amazing job when it comes down to freeing the world from widespread misconceptions by introducing everyone who’s willing to listen to the right data.

However, showing the right graphs is not always enough. If people do not possess the skills to critically interpret those beautifully presented data, those data might lead to even more misconceptions. In our rapidly digitalizing society people are faced with torrents of data every day. Hence, the need for everyone to possess basic data skills is more important than ever. And this is exactly where we can be of help. We figured that alongside teaching statistics to students we’d start at the core of public life: journalists.

Fighting data ignorance

Because who knows better how to reach the average news consumer than journalists? So we started talking to journalists to find out how they thought they might improve their work. ‘I would strongly recommend you to start with journalists,’ — Belgian data journalist Maarten Lambrechts told us.

Many journalists said the same thing: ‘we do not have the time and skills to understand and interpret data’. And that’s a shame, since journalists are fundamental in informing audiences worldwide about global challenges demonstrated in the seventeen Sustainable Development Goals.

Fighting data ignorance also means fighting for sustainable development: economic, social and environmental. But what if journalists get the data wrong, because they simply do not have the time to dig in or haven’t been taught statistics during their journalism studies?

Success story disputed

A good example of how a lack of data skills can go viral is the belief that vaccines cause autism. Vaccines are an absolute success story of public health (freeing a large part of the world of polio, measles and diphtheria). Yet due to a lack of critical interpretation of data, a lot of people now believe that vaccines cause autism.


Source: https://www.safeminds.org/mercury-autism/vaccines-and-autism/correlation-between-increases-in-autism-prevalence-and-introduction-of-new-vaccines/

For now let’s assume that this particular graph is correct (no data source mentioned, always something to be suspicious about). What is correct is that autism prevalence has been rising in the United States: it has doubled over the past decade. Judging by these numbers, vaccines and autism are correlated.

But this is where one of the essential data literacy skills comes in: correlation is not the same as causation.

In simpler terms: although the numbers are both going up, it does not mean that vaccines are causing autism. Notable (and fun) examples that correlations do not equal causations can be found on the website spuriouscorrelations.com

The only way to actually prove causation is by performing randomized control trials. So a decade ago, scientists decided to use these experiments to closely study the relation between vaccines and autism. By designing scientific experiments where people were not vaccinated or were vaccinated at fewer intervals and comparing this to the regular schedule, one can find out whether vaccines are causing autism. As can be read in these papers, they concluded that vaccines do not cause autism.

This example illustrates an important lesson for using data to update and refine your beliefs: interpretation is everything. And people need basic data literacy skills to draw the right conclusions and to understand interpretations made by others.

Passionate to help

As it is our mission to help everyone interpret data, we’re passionate to help people learn these skills. But which concepts do we expect everyone to master? What are the basic data literacy skills that are required to interpret the data concerning the sustainable development goals correctly?

First of all, what we need is a curriculum: a list of the most important skills. That is exactly why we are organizing a workshop during the UN World Data Forum. On January 16 at 3:15 pm we’ll be presenting our Collective curriculum to make civil society data literate in meeting room 2.61.

Firstly, we will create this particular list together with the attending participants (you?). Secondly, we will record the most successful explanations of participants on how to teach these concepts to people who (still) think they hate statistics. Of course, we will also share our experiences with teaching students statistics and share our vision on how we can help civil society become more data literate.

At the end of the conference, we will return to our tiny country below sea level with a free, crowd-sourced curriculum and corresponding teaching material that we will make available for everyone on this planet who wants to learn or teach basic data literacy skills!

Which data and statistics concepts/skills do you consider essential for every civilian and journalist? Do you have experiences helping people learn these skills?