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Magnitude and context

The United States recently reached a negative COVID-19 milestone – more than 1 Million deaths. I started to think about writing this blog post last December when the count was ‘only’ 800,000. How do we understand, appreciate and communicate large numbers?

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Magnitude and context
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Can Moneyball save the unicorn?

The answer is no, but Moneyball might be a nudge in the right direction. I was born and live in Greenland where unicorns (also known as narwhals) roam beneath the waves. In most other cultures, unicorns are horsy creatures who roam somewhere over the rainbow. In western cultures (as defined by those who read Harry Potter) killing a unicorn is an unspeakably evil deed. It used to be different, at least for the sea creature.

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Can Moneyball save the unicorn?
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Dangers of combining averages

Misinterpreting statistics is not a rare phenomenon and happens everywhere in the world. Very often it happens by the use of averages, as many understand statistics as the “science of averages,” but also with use of percentages, indices and other statistics.

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Dangers of combining averages
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Significant fitness

Last September, in my efforts to recover from a knee injury, I hired a personal trainer.  When we made the original plan, it ended on 31 December 2021 and he added the slogan “New Year, New You” to the plan. It’s the new year and, thankfully, big progress. I just committed for another three months and I’m really looking forward to every single upcoming workout. It’s fun! However, like many new things, there are new insights and … surprise surprise, some are statistical.

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Significant fitness
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Candy cane shortages and the importance of variation

It’s the holiday season in much of the world, and this year that means not just early sunsets and school breaks but also supply chain woes.  Everything, from puzzles and hot sauce to peppermint candy canes, is in short supply this month, as the global economy struggles to get items from point A to point B.

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Candy cane shortages and the importance of variation
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What is the use of lots of data when we don’t know what they mean?

There are lots of sources for data on Covid-19. Many of them just take data from government or hospital sources, and let you compare them however you want to. Others are a little more careful. Financial Times data journalists outline some of the problems:

Comparing the spread of coronavirus in different countries is difficult using the data being released by governments. Confirmed case counts depend heavily on the extent of countries’ very different testing regimes, so higher totals may simply reflect more testing.

Deaths are somewhat more reliable, but remain problematic because countries have different rules for what deaths to include in their official numbers. 

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What is the use of lots of data when we don’t know what they mean?
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Why is professional independence such a contentious issue?

The theme of professional independence in the production of official statistics is a subject that will never go away from the preoccupations of the international statistical community. It would seem that at any time somewhere in the world someone (usually a government) is trying to interfere against it. Last year, the attempt by the Trump administration to add a citizenship question to the 2020 US Population Census made the news in the US and worldwide, a decision more related to political considerations than to technical ones. In recent times, Argentina and Greece have also grabbed headlines around the world for altering figures in order to present a more favorable image of their economies, and a recent post on this blog raised awareness of a similar situation in Montenegro.

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Why is professional independence such a contentious issue?
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They are what they’re taught: The need for AI data curation ethics

Artificial intelligence (AI) refers to a bunch of computer algorithms used to build machines capable of carrying out tasks that typically require human intelligence. These machines learn particular tasks based on the data we generate. Similar to an old saying “We are what we eat,” the performance of an AI system depends on how and what we “teach” it.  Thus, the data collected and used are fundamental to training AI systems.

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They are what they’re taught: The need for AI data curation ethics
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Statistics in the Time of Politics

The well-known book Love in the Time of War tells a story of two people whose marriage is irrevocably shaped by the harsh realities of a war. Well, this is a story of two people whose lives are shaped by the harsh realities of politics now… and a bit more… just because of the commitment of the two people to protecting the integrity of official statistics.

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Statistics in the Time of Politics
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Horror aequi …

…or fear of repeating a word is very common in the media. It’s a journalistic compulsion that is detrimental to clear communication and understanding – but easy to avoid.

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Horror aequi …