Reading a Picture

Visual storytelling

Visualising data helps understanding facts.
Sometimes it’s very easy to understand a graph; sometimes it’s necessary to read it and to study it to discover unknown territory.

Such graphs are little masterpieces. Here’s one of these and I am sure the authors had more than one iteration and discussion while creating it.
The graph tells the story of the average disposable income and savings of households in Switzerland, published by the Swiss Federal Statistical Office FSO.


The authors kindly give a short explanation:

How to read this graph.
In one-person households aged 64 or under, the upper-income group has a disposable income of CHF 8487 per month and savings of CHF 2758 per month. Representing 4.0% of all households, this income group corresponds to a fifth of one-person households aged 64 or under (20.1%)

There’s another nice graph, a little bit less elaborated, also explained by the authors:


Statistics ♥

But there’s one thing that is not explained:

snip_poverty-cithe confidence interval!

‘A confidence interval gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data,‘ and the above poverty data are from a sample of ‘approximately 7000 households, i.e. more than 17,000 persons who are randomly selected…’.
The confidence intervals for the mean give us a range of values around the mean where we expect the “true” (population) mean is located (with a given level of certainty, see also Elementary Concepts). ….. as we all know from the weather forecast, the more “vague” the prediction (i.e., wider the confidence interval), the more likely it will materialize. Note that the width of the confidence interval depends on the sample size and on the variation of data values…..’

Khan Academy gives lectures about topics like confidence intervals, sampling, etc.


Which one ?

The above graphs use just one of multiple possibilities for visualising data.


Severino Ribecca’s Data Visualisation Catalogue is one of many websites trying to give an overview. And there’s the risk to get lost in these compilations.

snip_swimring                            ©

Statistics is Dead – Long Live Statistics

To be an expert in a thematic field!

Lee Baker wrote an article that will please the whole community of official statistics where specialists of many thematic fields (and not alone statisticians or mathematicians or … data scientists) are collecting, analysing, interpreting, explaining and publishing data.
It’s this core message that counts:
“… if you want to be an expert Data Scientist in Business, Medicine or Engineering”  (or vice versa: An expert statistician in a field of official statistics like demography, economy, etc.)  “then the biggest skill you’ll need will be in Business, Medicine or Engineering…. In other words, …. you really do need to be an expert in your field as well as having some of the other listed skills”

Here is his chain of arguments:

“Statistics is Dead – Long Live Data Science…

by Lee Barker

I keep hearing Data Scientists say that ‘Statistics is Dead’, and they even have big debates about it attended by the good and great of Data Science. Interestingly, there seem to be very few actual statisticians at these debates.

So why do Data Scientists think that stats is dead? Where does the notion that there is no longer any need for statistical analysis come from? And are they right?

Is statistics dead or is it just pining for the fjords?

I guess that really we should start at the beginning by asking the question ‘What Is Statistics?’.
Briefly, what makes statistics unique and a distinct branch of mathematics is that statistics is the study of the uncertainty of data.
So let’s look at this logically. If Data Scientists are correct (well, at least some of them) and statistics is dead, then either (1) we don’t need to quantify the uncertainty or (2) we have better tools than statistics to measure it.

Quantifying the Uncertainty in Data

Why would we no longer have any need to measure and control the uncertainty in our data?
Have we discovered some amazing new way of observing, collecting, collating and analysing our data that we no longer have uncertainty?
I don’t believe so and, as far as I can tell, with the explosion of data that we’re experiencing – the amount of data that currently exists doubles every 18 months – the level of uncertainty in data is on the increase.

So we must have better tools than statistics to quantify the uncertainty, then?
Well, no. It may be true that most statistical measures were developed decades ago when ‘Big Data’ just didn’t exist, and that the ‘old’ statistical tests often creak at the hinges when faced with enormous volumes of data, but there simply isn’t a better way of measuring uncertainty than with statistics – at least not yet, anyway.

So why is it that many Data Scientists are insistent that there is no place for statistics in the 21st Century?

Well, I guess if it’s not statistics that’s the problem, there must be something wrong with Data Science.

So let’s have a heated debate…

What is Data Science?

Nobody seems to be able to come up with a firm definition of what Data Science is.
Some believe that Data Science is just a sexed-up term for statistics, whilst others suggest that it is an alternative name for ‘Business Intelligence’. Some claim that Data Science is all about the creation of data products to be able to analyse the incredible amounts of data that we’re faced with.
I don’t disagree with any of these, but suggest that maybe all these definitions are a small part of a much bigger beast.

To get a better understanding of Data Science it might be easier to look at what Data Scientists do rather than what they are.

Data Science is all about extracting knowledge from data (I think just about everyone agrees with this very vague description), and it incorporates many diverse skills, such as mathematics, statistics, artificial intelligence, computer programming, visualisation, image analysis, and much more.

It is in the last bit, the ‘much more’ that I think defines a Data Scientist more than the previous bits. In my view, if you want to be an expert Data Scientist in Business, Medicine or Engineering then the biggest skill you’ll need will be in Business, Medicine or Engineering. Ally that with a combination of some/all of the other skills and you’ll be well on your way to being in great demand by the top dogs in your field.

In other words, if you want to call yourself a Data Scientist you really do need to be an expert in your field as well as having some of the other listed skills.

Are Computer Programmers Data Scientists?

On the other hand – as seems to be happening in Universities here in the UK and over the pond in the good old US of A – there are Data Science courses full of computer programmers that are learning how to handle data, use Hadoop and R, program in Python and plug their data into Artificial Neural Networks.

It seems that we’re creating a generation of Computer Programmers that, with the addition of a few extra tools on their CV, claim to be expert Data Scientists.

I think we’re in dangerous territory here.

It’s easy to learn how to use a few tools, but much much harder to use those tools intelligently to extract valuable, actionable information in a specialised field.

If you have little/no medical knowledge, how do you know which data outcomes are valuable?
If you’re not an expert in business, then how do you know which insights should be acted upon to make sound business decisions, and which should be ignored?

Plug-And-Play Data Analysis

This, to me, is the crux of the problem. Many of the current crop of Data Scientists – talented computer programmers though they may be – see Data Science as an exercise in plug-and-play.

Plug your dataset into tool A and you get some descriptions of your data. Plug it into tool B and you get a visualisation. Want predictions? Great – just use tool C.

Statistics, though, seems to be lagging behind in the Data Science revolution. There aren’t nearly as many automated statistical tools as there are visualisation tools or predictive tools, so the Data Scientists have to actually do the statistics themselves.

And statistics is hard.
So they ask if it’s really, really necessary.
I mean, we’ve already got the answer, so why do we need to waste our time with stats?


So statistics gets relegated to such an extent that Data Scientists declare it dead.”

The original article and discussion –>here

About the Author

Lee Baker is an award-winning software creator with a passion for turning data into a story.
A proud Yorkshireman, he now lives by the sparkling shores of the East Coast of Scotland. Physicist, statistician and programmer, child of the flower-power psychedelic ‘60s, it’s amazing he turned out so normal!
Turning his back on a promising academic career to do something more satisfying, as the CEO and co-founder of Chi-Squared Innovations he now works double the hours for half the pay and 10 times the stress – but 100 times the fun!”

This post is taken from datascience.central and has been published previously in Innovation Enterprise and LinkedIn Pulse

For a fact-based Worldview


Hans Rosling, co-founder and promoter of the Gapminder Foundation and of fights with statistics against myths (‘Our goal is to replace devastating myths with a fact-based worldview.’) and tries to counterbalance media focussing on war, conflicts and chaos.

Here one more example (and this in a media interview…): ‘You can’t use media if you want to understand the world’ (sic!)

And this statement on; ‘Statistical facts don’t come to people naturally. Quite the opposite. Most people understand the world by generalizing personal experiences which are very biased. In the media the “news-worthy” events exaggerate the unusual and put the focus on swift changes. Slow and steady changes in major trends don’t get much attention. Unintentionally, people end-up carrying around a sack of outdated facts that you got in school (including knowledge that often was outdated when acquired in school).’



Dürer’s Rhinoceros and Statistics

Mixing Dürer’s Rhino with Statistics might sound a little bit strange.

Dürer’s Rhinoceros – Wikipedia, the free encyclopedia.

But in an epistemological perspective there’s a point.  Dürer never saw a Rhinoceros; he created it – in accordance with some information he got- in the process of drawing. Statistics – in a sense –  do the same and this even with objects which do not exist in ‘reality’.

This topic is itself an object in several studies. So the Norvegians Rudinow Saetnan, Heidi Mork Lomell and Svein Hammer treat it in their reader ‘The mutual construction of statistics and society‘.

‘How does the act of counting affect the world? How does it change the objects counted, change the lifes of those who count (double entendre intended)? …  Our argument, briefly stated, is that society and the statistics that measure and describe it are mutually constructed.  This argumcnt addresses two counterarguments from seemingly opposite directions. On the one hand, we oppose the notion that statistics are simple, straighthforward, objective descriptions of society, gathered from nonparticipant points of observation…. Like all othcr specific forms of viewing, it is a social act. Counting acts in and upon the social world. Of course, this also means that not counting has an effect on the aspects of the world we (do and/or don’t) count. ….
On the other hand, we also oppose the notion that statistics and/or society are mere fictions, to bc invented at will.’  (Introduction, p.1)

And in its alltime classic ‘The politics of Large NumbersAlain Desrosières treats the same question: ‘ … it is difficult to think simultaneaously that the objects being measured really do exist, and thatt this is only a convention’ (p.1)

And here’s the real Rhinoceros (Indian rhinoceros (Rhinoceros unicornis), Panzernashorn )

Statistics are not so bad .. -;) .

IMAODBC 2010: And the winner is . . .

The Bo Sundgren Award of the International Marketing and Output Database Conference IMAODBC 2010 in Vilnius goes to Vincenzo Patruno from Statistics Italy ISTAT. In his presentation about Data Sharing Vincenzo Patruno demonstrates the use of widgets for the dissemination of statistical informations. Widgets are small pieces of code which can be embedded in a website and interact with an application, i.e. a database. Once embedded the information they provide is always updated automatically whenever the application itself is updated.
See some examples on Vincenzos Blogespecially the post How to Share a whole application on the Web. The small table with figures for Rome on the right hand-column of his blog is such a widget.