Group items tagged

A good example of poor reporting of a medical study, the Daily Mail claimed that "Diets laden with butter, cream and cheese 'can help combat surge in type 2 diabetes' - but the evidence does not support that argument - looking at the evidence, it's clear there is a small quantifiable difference between the two groups analysed, and that the study focuses on a short period of time.

As an epidemiologist, I am interested in disease - and more specifically, who in a population currently has or might get that disease. What is their age, sex, or socioeconomic status? Where do they live? What can people do to limit their chances of getting sick? Questions exploring whether something is likely to happen or not can be answered with quantitative research. By counting and measuring, we quantify (measure) a phenomenon in our world, and present the results through percentages and averages. We use statistics to help interpret the significance of the results. While this approach is very important, it can't tell us everything about a disease and peoples' experiences of it. That's where qualitative data becomes important.

Nice piece on computational thinking and data journalism. For example... This story, published in the UK tabloid newspaper The Mirror, is a great example of understanding how a computer might 'see' information and be able to help you extract a story from it. The data behind the story is a collection of over 300,000 pieces of sheet music. On paper that music would be a collection of ink on paper. But because that has now been digitised, it is now quantified. That means we can perform calculations and comparisons against it. We could: Count the number of notes Calculate the variety (number of different) of notes Identify the most common notes Identify the notes with the maximum value Identify the notes with the minimum value Calculate a 'range' by subtracting the minimum from the maximum The journalist has seen this, and decided that the last option has perhaps the most potential to be newsworthy - we assume some singers have wider ranges than others, and the reality may surprise us (a quality of newsworthiness).

Protests against racial injustice and the COVID-19 pandemic have exposed racial inequalities rife within social and economic systems around the world. Fed up with police brutality and systemic racism against African Americans and other racialised groups, people staged protests against racial injustice in all 50 states across the United States.Apart from these examples, however, there is surprisingly little data or discourse about the impact of the disease on racial and ethnic minorities in the rest of Europe. This silence speaks volumes about Europe's approach to racism.The vast majority of EU member states do not use the concept of race or ethnic origin in data collection, in spite of policies like the European Racial Equality Directive and the Employment Equality Directive which prohibit racial or ethnic discrimination. France outright prohibits it.Without disaggregated data, it is virtually impossible to quantify the extent of discrimination experienced by racial and ethnic groups or the impacts of COVID-19 on their lives.

Nice article, don't just show the data, tell us what it means "I use a Misfit activity tracker to count my steps. The Misfit app does a decent job of showing me step counts per day and every month, misfit also sends me a summary of the previous month's activity. Unfortunately, the numbers in that summary are presented without any context, making that summary almost entirely useless."

"Fitness trackers out of step when measuring calories, research shows," The Guardian reports. An independent analysis of a number of leading brands found they were all prone to inaccurate recording of energy expenditure.