A core skill for scientists and engineers is analyzing data objectively. This is important when reviewing experimental results and other people's work. This is an important life skill as well, since interpreting data is critical for understanding fields like economics, politics, and public policy. As an example, consider the following statement from an editorial on agriculture in the New York Times:
A four-acre farm in the United States nets, on average, $1,400 per acre; a 1,364-acre farm nets $39 an acre.
Why might that be true? There are a number of possibilities. For example, it is possible that the four-acre farms are more efficient, so they produce 36 times more output per acre then the larger farms. However, a productivity difference that large seems extremely unlikely. It seems more probable that when growing high value crops, farms can afford to be smaller. Conversely, if the climate is only appropriate for growing low-cost grain, a huge farm is needed to support the farmer. There is a world of difference between the economics of a small vineyard growing grapes for wine and a huge wheat farm in the American plains.
The problem is that the author uses this data to support his argument that "small farms are the most productive on earth." However, this is not a valid comparison. A scientific comparison needs to hold as many variables constant as possible. In this case, we need to examine farms of different sizes growing the same crops in the same regions. These numbers, averaged across the United States, don't tell us anything.
Examining data with a critical eye is a skill that I have learned in graduate school by critiquing technical papers. However, this seems like something that should be an important part of the curriculum in high school and undergraduate math and science classes. Understanding data objectively is important in the modern world. As Larry Page said when asked what background people need to drive change: "you need an engineering education where you can evaluate the alternatives."