Statistics for philosophers

Truth and cause in Mathematics, natural science and Philosophy.

Most students and professors in Philosophy and the Humanities in general, consider Statistics a boring mathematical tool of little or no relevance to their disciplines.

Yet philosophers for thousands of years have been struggling with the concepts of Truth and Cause. The search for Truth has been in the core of philosophical discussions since the the time of the presocratic philosophers. Cause is an concept currently vividly discussed on web fora.

A mind-boggling number of articles and books populate libraries and databases. Famous names like David Hume, John Locke, Karl Popper along with contemporary renown professors with their student followers churn out wisdom by the ton: Truth is, about truth, Cause and cause qualified by epithets and ad verecundiam adornments!

For a natural scientist all of these activities carry the hue of queerness. Rarely the word cause is used in scientific research as it is meaningless. A graph, like a dose-respone graph, is all that can be said. An observed event is said to be a function of/ That is a precise statement with no ghost-like allusions.

Truth , as regards empirical phenomena, is simply and perhaps finally defined in Statistics and natural science. Any statement regarding a finding, the occurrence of a natural phenomenon, is followed by a probability statement : what is the probability that an event occurring under defined circumstances may be a chance event. This probability is expressed as p<0.05 or p>0.05 or p<0.01 or other.

In all natural science, a finding, the occurrence of an event is not characterized as true, rather it is qualified as reliable or not. In other words, it is announced with a Humean hue: It is not certain that it will occur again, that it is true. All that we can say is that there is a calculated probability that it may not occur again.

All of the above do not pertain to formal science where Truth and necessity are legitimate concpts.