Results management can be defined as interventions used to change the actual or interpretation of a result. The most recent example in the financial area is Enron, where revenue numbers were subject to upwards revision via dubious accounting interventions.
Results management is distinct from ‘results-based management’ – a legitimate management approach focusing on achieving outcomes, implementing performance measurement, learning and changing, and reporting performance.
Luboš Motl of The Reference Frame identified a possible example of results management in Science in his article Borehole climate reconstructions & hockey stick revolution in 1998. By way of background,
Boreholes were identified as decoding past temperatures by examining the temperatures profiles down the hole. Using this method the authors published the success of this result, and summarized the scientific finding that temperatures were higher than the present during a number of periods in the past.
Predicting global temperatures seems to be entering general awareness as a worthwhile exercise. As I have published about recently, I think climate models are inadequately validated, confidence in the skill of models to forecast global warming is vastly exaggerated, and current skill is not enough to serve useful purposes. I thought I would tabulate some of the various predictions as I come across them. This is a fair test, as the future is unknown, and at the end of the year we can see whose is most accurate. Read the rest of this entry…
As in the previous post about recent plummeting global temperatures, I want to look at the statistics of the drop, and determine its significance. The sort of questions of interest are, how improbable is a fall in temperatures of that magnitude of a 12 month period? After all, it is irresponsible to report alarming results without demonstrating the statistical significance. Unfortunately it is a common practice, for example, see record high temperatures from NASA.
The statistical setup for answering the question is encoded in the question. As we are only looking at falls in temperature, this should be a one-tailed test. The data we need are the twelve-month changes in global temperature anomalies, of which there are twelve every year to compare against the previous year. We then need the area of the distribution curve for these results, up to and including the value in question, -0.5906 in the case of the HadCRU data.
As reported by Anthony Watts at his blog post at Watt’s Up With That, global surface temperatures plummeted in the month of January. All four major sources of temperature anomoly data reported sharp drops, averaging -0.6405C. It is also reported that the large contribution to this value is from Northern Hemisphere land temperatures showing a huge drop of 2.4C from last January.
While looking at a roadside cutting in order to evaluate options for treating it for erosion, I thought it would be interesting to
gather the data and develop a 3D model in R. The process can be confusing the first time, as the data need to be massaged to meet the requirements of the interpolative algorithm that converts scattered heights to a regular grid, which can then be visualized.
Below is the process I used, and it was relatively quick.
