In a galaxy far far away and long long ago in a remote place called Planet Houston I was teaching a general course on navigation. I was covering data adjustment, signal processing and navigation system calibration when one member of the class suggested that what I was teaching was “fudging the data”.
I asked a few questions and realized to my horror that several others in the class had clearly misunderstood the subject matter and agreed that data processing and standard data adjustment techniques were little more than sophisticated cheating techniques to get a desired result. Friedrich Carl Gauss would turn in his grave.
The entire field of seismic exploration in the service of the search for oil rests on advanced data processing algorithms to extract meaningful information from vast amounts of extremely complex data. The view of some elements of my class was tantamount to believing the entire oil exploration business was built on a foundation of lies. By extension; they effectively believed that virtually all of scientific measurement and mathematics as applied to scientific data was little more than a farrago of chicanery and falsehoods. I was gob smacked.
Shit! What to do?
I decided right then and right there to add a little remedial math(s) to the navigation course because it seemed to me that my entire syllabus was a waste of time if its foundations were so badly undermined by misunderstandings and frightening mathematical ignorance.
I have seen many posts and articles recently that deny climate change and use seemingly legitimate arguments to do so. One of the most common is to show that supposedly conniving, cheating scientists have falsified temperature data in the interests of their hysterical and dishonest claims about climate change. This is the same mathematical and scientific ignorance exhibited by a few of my students several decades ago except that this time the future of the planet is at stake.
There isn’t space here to do a remedial maths or science course but perhaps a simple example might serve to illustrate the scientific necessity for measurement adjustments and data processing before using raw data to more closely determine scientific truths.
Imagine, if you will, a world wide project to determine the average height of five year old children around the world. All measurements are to be made with metal measurement tapes and the results sent to a central point for collation. Simple! What could possibly go wrong?
So all results are collected and the average height of 5 year old kids is calculated by adding all the heights and dividing by the number of kids. So far so good.
Then some curious social worker wonders if the average height of a 5 year old varies from one place to another. The results are recomputed for each geographical area and it turns out that there are variations. Why?
Some smart-arse scientist recognizes that since the measurements were made with metal tapes and all metal expands with increasing temperature, the kids measured in hot climates such as northern Australia would seem to be shorter than kids measured in the Arctic conditions of northern Canada.
Fortunately all height measurements were submitted with location and temperature data thus allowing the entire data set to be corrected for temperature variations.
Then some super nit picky awkward bugger asks “are all the steel tapes identical ?” The tapes are recalled and it transpires that one batch of measuring tapes were 2 mm short due to a manufacturing error. All measurements made with these tapes can be corrected by adding 2 mm to all heights.
Get the picture? All measurements and all data sets contain errors of one sort or another; offsets, biases, scaling errors, random noise and even mistakes. The art of measurement and the scientific use of measurements is to detect and eliminate those errors. Better results from older data can be obtained over time by applying better techniques and corrections for errors that might not have been known at the time of the original measurements.
So it is with measuring the heights of children and so it is with measuring global temperatures. Refining temperature data in the light of new knowledge is not fudging, it is good science.