Forfatter av avsnitt: Danielle J. Navarro and David R. Foxcroft
Hypotesetesting
Induksjonsprosessen er prosessen med å anta den enkleste loven som kan bringes til å harmonere med vår erfaring. Denne prosessen har imidlertid ikke noe logisk fundament, men kun et psykologisk. Det er klart at det ikke finnes noen grunn til å tro at det enkleste hendelsesforløpet virkelig vil skje. Det er en hypotese at solen vil stå opp i morgen, og det betyr at vi ikke vet om den vil stå opp.—Ludwig Wittgenstein[1]
In the last chapter I discussed the ideas behind estimation, which is one of the two “big ideas” in inferential statistics. It is now time to turn our attention to the other big idea, which is hypothesis testing. In its most abstract form, hypothesis testing is really a very simple idea. The researcher has some theory about the world and wants to determine whether or not the data actually support that theory. However, the details are messy and most people find the theory of hypothesis testing to be the most frustrating part of statistics. The structure of the chapter is as follows. First, I will describe how hypothesis testing works in a fair amount of detail, using a simple running example to show you how a hypothesis test is “built”. I will try to avoid being too dogmatic while doing so, and focus instead on the underlying logic of the testing procedure.[2] Afterwards, I will spend a bit of time talking about the various dogmas, rules and heresies that surround the theory of hypothesis testing.