Two unhealthy submissions from readers

Josh hated this "dataless visualization" from ABC. (link; warning: ads). Here are his comments:

The report has planes leaving China, landing across the globe and instantly infecting us all with bird flu.  It doesn't do a good job explaining how and the rate pandemics actually spread.  However, it does do a good job scaring us all.

The entire flu pandemic theater is unscientific. It is based on the 100-year flood type of argument, with scientists claiming that we are "overdue" for some catastrophe. Reminds me of earthquake forecasting, covered by Nate Silver in his book. It is possible to predict the average frequency of, but virtually impossible to predict the timing of rare natural disasters.

The 100-year flood type calculations is based on averaging a small number of events over a very long time scale. There is no reason why these events should be spread out evenly over time (i.e. one event every 100 years).

This is a fallacy of "law of small numbers": if one throws a fair coin 10 times, one shouldn't expect exactly 5 heads, as the distribution of heads should look like the chart on the right. The chance of exactly 5 heads is only 25%.

Also, doctors keep me honest but I believe only one type of mutation, i.e. the one that makes the virus able to pass from human to human, has a chance of causing a pandemic. So it is wrong to say that "if the virus mutates," a pandemic will result. In addition, in the past, some viruses were able to pass from human to human but the rate of infection was not fast enough, and they failed to lead to a pandemic.

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Daniel L. did not like the map shown below, from a research article on female mortality rate in the U.S., via Jezebel.

I was amused by what the blogger at Jezebel was able to take in from the map. Her post started with a huge version of the map, under which she said:

Mortality rates are rising in 43% of U.S. counties, as illustrated by this map from health researcher Bill Gardner.

Mortality rate is a statistic about the population. The map is an illustration of geographical area (distorted by the map projection). The map carries no information about population at all. Thus, it is not the right chart to display population data.

The statistic itself is poorly chosen. What does 43% of counties mean? Some counties have few people while others are very densely populated. New York County is barely visible on this map yet it has the heaviest weight on the average. 

According to the CDC data, the death rate, age-adjusted, for women has been decreasing over time. So, the backward motion in those 43% of counties is somehow compensated for by forward progress in the other 57% of the counties, it appears.

Maybe the average for the whole country masks some local patterns. The cited map doesn't help because it assumes that the importance of the mortality rate is proportional to the geographical size of the county, when the right comparison should be the population of women in the county.