The Art of Statistics: Learning from Data

by David Spiegelhalter

The idea of a null hypothesis now becomes central: it is the simplified form of statistical model that we are going to work with until we have sufficient evidence against it. In the questions listed above, the null hypotheses might be: The daily number of homicides in the UK do follow a Poisson distribution. The UK unemployment rate has remained unchanged over the last quarter. Statins do not reduce the risk of heart attacks and strokes in people like me. Mothers’ heights have no effect on sons’ heights, once fathers’ heights are taken into account. The Higgs boson does not exist. The null hypothesis is what we are willing to assume is the case until proven otherwise. It is relentlessly negative, denying all progress and change. But this does not mean that we actually believe the null hypothesis is literally true: it should be clear that none of the hypotheses listed above could plausibly be precisely correct (except possibly the non-existence of the Higgs boson). So we can never claim that the null hypothesis has been actually proved: in the words of another great British statistician, Ronald Fisher, ‘the null hypothesis is never proved or established, but is possibly disproved, in the course of experimentation. Every experiment may be said to exist only in order to give the facts a chance of disproving the null hypothesis.’2 There is a strong analogy with criminal trials in the English legal system: a defendant can be found guilty, but nobody is ever found innocent, simp...

Null hypthesis is like english law of trial