There is no New Thing under the Sun – Yes and No

Twitter reminded me that there’s #NTTS2017 going on, Eurostat’s biennial scientific conference on New Techniques and Technologies for Statistics (NTTS).

The opening session also focused on official statistics and its actual and future role in a world of data deluge and alt-facts. What will be Official Statistics in 30 years?
In Diego Kuonen’s presentation and discussion on ‘Big Data, Data Science, Machine Intelligence and Learning’ I could hear an answer to this question reminding me of a text in the Bible: “… that [thing] which is done is that which shall be done: and there is no new thing under the sun”.
And this not to be understood in a static but in a dynamic interpretation:
The work statistical institutions are doing today will be the same that they will do tomorrow … BUT a work adapted to the changing context.
The algorithms (understood in a broader sense as ‘a set of rules that precisely defines a sequence of operations ->‘) used in collecting, analyzing and disseminating data will be changing, manual work will / must be replaced by automation, robots. But the core role of being a trusted source of data-based and (in all operations) transparently produced information serving professional decision making will remain.
The challenge will be that these institutions
– are known,
– are noted for their veracity,
– are consulted
and with all this can play their role.
In this fighting to be heard humans will always play a decisive part.
That’s a clear message (as I understood it) of a data scientist looking ahead.
.
PS. A step towards automation consists of preparing and using linked data. See the NTTS 2017 satellite session “Hands-on workshop on Linked Open Statistical Data (LOD)”

You Can See in Numbers

‘We are extremely sad to announce that Professor Hans Rosling died this morning. Hans suffered from a pancreatic cancer which was diagnosed one year ago. He passed away early Tuesday morning, February 7, 2017, surrounded by his family in Uppsala, Sweden.’ Anna R. Rönnlund & Ola Rosling, Co-founders of Gapminder. He died aged 68.

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Hans Rosling, Geneva, 2009-10-30

In 2009, the Swiss Statistics’ Meeting took place in Geneva, Switzerland. Hans Rosling was there and his talk’s topic: ‘Unveiling the beauty of statistics’. He wanted data to be free, free from legal and technical barriers. His ambition – and his success – was to disseminate these data beautifully … in order to change the world.

A difficult task. In an interview in the Guardian, in 2013: “It’s that I became so famous with so little impact on knowledge,” he says, when asked what’s surprised him most about the reaction he’s received. “Fame is easy to acquire, impact is much more difficult. …. He’s similarly nonplussed about being a data guru. “I don’t like it. My interest is not data, it’s the world. And part of world development you can see in numbers.”  (Taken from the Guardian interview 2013).

And that’s why statistics and the world need more people like Hans Rosling – more than ever!

Post Post-Truth

postfaktisch

‘Fake-news’ and ‘post-truth’ (postfaktisch) are the words dominating today many discussions about truth in communication.

' ... in post-truth [post] has a meaning more like ‘belonging to a time in which the specified concept [truth] has become unimportant or irrelevant’' (https://www.oxforddictionaries.com/press/news/2016/11/15/WOTY-16).

False information or even lies are not new in the information business. And therefore many, and many more websites help to separate wrong from right:

The Reporters’ Lab maintains a database of global fact-checking sites.

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And Alexios Mantzarlis ‘collected 366 links, one for each day of the year …  to understand fact-checking in 2016′.

 

Official Statistics’ Ethical Codex

Officials Statistics collect, analyze and disseminate statistical information since long and are also confronted with wrong citations, misuse of statistics and lies. Many of the ethical codices of official statistics recommend acting against such false information.

‘In 1992, the United Nations Economic Commission for Europe (UNECE) adopted the fundamental principles of official statistics in the UNECE region. The United Nations Statistical Commission adopted these principles in 1994 at the global level. The Economic and Social Council (ECOSOC) endorsed the Fundamental Principles of Official Statistics in 2013; and in January 2014, they were adopted by General Assembly. This recognition at the highest political level underlines that official statistics – reliable and objective information – is crucial for decision making.’

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Two paragraphs are of special interest:

‘ 2. Professional standards and ethics
To retain trust in official statistics, the statistical agencies need to decide according to strictly professional considerations, including scientific principles and professional ethics, on the methods and procedures for the collection, processing, storage and presentation of statistical data.’

AND:

‘4. Prevention of misuse
The statistical agencies are entitled to comment on erroneous interpretation and misuse of statistics.’

The European Statistics Code of Practice says in principle 1:

1.7: The National Statistical Institute and Eurostat and, where appropriate, other statistical authorities, comment publicly on statistical issues, including criticisms and misuses of statistics as far as considered suitable.

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N.B: Wikipedia’s page on Misuse of statistics presents a broad view how readers can be fooled by many types of misuse.

It’s dissemination – …

False – and especially deliberately false – information as a weapon in manipulating decisions isn’t new either. But new is how such information spreads: with the help of social media dissemination gains a new level  (some say like earlier Gutenberg’s printing press ).

Anne Applebaum gives a practical illustration of how it can work:

‘I was a victim of a Russian smear campaign. I understand the power of fake news.

It was a peculiar experience, but I learned a lot. As I watched the story move around the Web, I saw how the worlds of fake websites and fake news exist to reinforce one another and give falsehood credence. Many of the websites quoted not the original, dodgy source, but one another. There were more phony sites than I’d realized, though I also learned that many of their “followers” (maybe even most of them) are bots — bits of computer code that can be programmed to imitate human social media accounts and told to pass on particular stories.
….
But it is also true that we are living through a global media revolution, that people are hearing and digesting political information in brand-new ways and that nobody yet understands the consequences. Fake stories are easier to create, fake websites can be designed to host them, and social media rapidly disseminates disinformation that people trust because they get it from friends. This radical revolution has happened without many politicians noticing or caring — unless, like me, they happened to have seen how the new system of information exchange works.’

 

2017

May 2017 become the year of people who know about the power and the dangers of misleading information!
My best wishes to the colleagues in Official Statistics and their professional producing and disseminating information …. and perhaps statistical dissemination will need to be more active on social media, too.

 

 

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.

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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:

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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…’.
Or:
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.

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Which one ?

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

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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                            © listverse.com

LOD MOOC

Massive Open Online Courses (MOOC) are available worldwide and offer tons of topics, also about Linked Open Data (LOD). An easy way to enter the semantic web.

Two examples:

HPI

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The Hasso Plattner Institute, Potsdam provides, for some years now, a course in Linked Data Engineering with a certificate. I did it some years ago and enjoyed it.

FUN (INRIA)

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The French platform FUN offers a LOD course, too. (Thanks to Adrian at zazuko.com for the hint)

And books

Step by step Bob DuCharme introduces RDF, SPARQL, LOD …

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.

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IMAODBC 2016: And the winner is…

The Best Presentation Award of the International Marketing and Output Database Conference IMAODBC 2016 in Gozd Martuljek, Slovenia goes to Susanne Hagenkort-Rieger and her team from DESTATIS (Statistisches Bundesamt, Germany).

In her presentation Susanne highlighted the importance of web search statistics  and why intuition when emphasizing selected statistical data is often not sufficient. To achieve relevance and accessibility of most popular statistical data we should not ignore what the web search data say.

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Presentation is available at the IMAODBC 2016 website…

A few facts about IMAODBC 2016 as presented in the second best presentation by Corey Jenkins, USDA – Foreign Agricultural Service, U.S.A.:

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