The null hypothesis is the mathematical relationship that holds if the theory being tested is false. So for AGW we first look if the globe is warming.
AGW is the theory, so the alternate hypothesis has to be:
H1: temperatures are increasing.
To test this we work out a null hypothesis, one that holds if the theory is null. The null hypothesis thus must be testable and falsifiable. For AGW the null hypothesis must be:
H0: temperatures are stable.
We need a test, a statistical test. We could try correlation/regression etc but I propose Chi Squared, monthly global temperatures from 1850–2017. We do this, we find temperatures are not stable so we reject the null hypothesis. Looking at the data we see temperatures have actually increased. We therefore accept the alternate hypothesis.
Don’t believe me? Try this definition of the null hypothesis:
Quote:The null hypothesis is essentially the "devil's advocate" position. That is, it assumes that whatever you are trying to prove did not happen (hint: it usually states that something equals zero). For example, the two different teaching methods did not result in different exam performances (i.e., zero difference). Another example might be that there is no relationship between anxiety and athletic performance (i.e., the slope is zero). The alternative hypothesis states the opposite and is usually the hypothesis you are trying to prove (e.g., the two different teaching methods did result in different exam performances). Initially, you can state these hypotheses in more general terms (e.g., using terms like "effect", "relationship", etc.), as shown below for the teaching methods example:
https://statistics.laerd.com/statistical-guides/hypothesis-testing-3.phpThis is the
definition of the null hypothesis I have posted. All three definitions are congruent.
You fools can apologise to me any time you like.
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