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The problem with statistics

Modern Farmer had an interesting story this week about agricultural statistics–the counts of acreage, farm size, crops planted, and other measures of agricultural production.  In the story, the author points out that from one census to the next changes in how the counts are made can make it look like tremendous changes have occurred in land use and other factors while in reality the changes may have been fairly small.  You can read the story by clicking here.

Similar issues come up when dealing with calculations of climate statistics.  Climatologists want to present a long-term, uniform set of measurements of temperature, precipitation and other variables to get a true picture of what changes have been seen over time.  However, you can’t just take the raw data and expect to get a true picture.  Climatologists have to factor in changes in the number of stations, movements of the stations over time, changes in instruments and measuring techniques, and changes in the distribution of stations across the globe to get an accurate and uniform measure of climate changes.  They do this by adjusting the data to reduce the impacts of  “false” changes in climate caused by station changes like moves, growth of urban areas, and the time of observation.  These changes might make it look like the climate is changing but in fact, the changes are really caused by other factors related to the station itself, not the climate it is measuring.  It’s not an easy job to take all of these factors into account, but overall the scientists who are doing this are trying hard to produce the most accurate datasets available.  A lot of the arguments from those who are skeptical about trends in climate databases deal with how the corrections in the data are made, but even skeptics who have tried their own corrections to the databases have come up with similar results, leading to more confidence in the accuracy of the data and the trends they show.