Any chance someone can explain null hypothesis?
If my hypothesis is that age affects physical activity – after completing a random survey and this point is not proved – is this my null hypothesis? Then I wouldn’t reject it, so what do I do?

http://en.wikipedia.org/wiki/Null_hypothesis has some info... but I can't say that it's entirely clear to me but maybe it will help you!

I don't quite understand it either, but it seems to suggest that a null hypothesis is one that you assume and try to prove correct, until you find that the data you collect favours an alternative hypothesis more than the null hypothesis.

I suppose that it does prove a point but I can understand why it is contraversial.

I'd have to agree with Capuchin. Null, meaning empty, would lead me to believe that it means you have nothing to support your hypothesis

The null hypothesis is usually set as the *opposite* of what you expect to prove. It is usually a statistical construct, but can be used in all aspects of the scientific method. Essentially, the idea is that while you can never unequivocally PROVE any hypothesis, you can disprove a null hypothesis, and thereby provide support to your alternate hypothesis.

If the null hypothesis is not true, then the alternative hypothesis must be true.

The null hypothesis is a statistical hypothesis that contains a statement of equality:

\leq, \;\ =, \;\ \geq

The alternate hypothesis is the complement of the null hypothesis. It is a statement that must be true if the null is false and it contains a statement of inequality:

<, \;\ \neq, \;\ >

No matter which hypothesis represents the claim, you always start a hypothesis test by assuming that the equality statement in the null is true. So, when you perform a hypothesis test, you make one of two decisions:

1. Reject the null hypothesis
2. Fail to reject the null hypothesis

Here's a statistics forum link:

www.statstalk.com

If the null hypothesis is not true, then the alternative hypothesis must be true.

Important to note that this only holds if the hypotheses are mutually exclusive AND cover all the possible subsets. It is a common mistake made by novices to stats and other scientists.

More importantly, in stats, rejecting the null is almost never an all-or-none proposition. That is, as you have put it "If the null hypothesis is not true", is usually actually phrased as "To x degree of certainty", or something like that.

To further explain this, if my p value is less than x (usually 0.05 or 0.01) then I reject the null. That doesn't mean that the null hypothesis is not true. Rather, it means that 95% (or 99%) of the time that this threshold is exceeded, the null hypothesis can correctly be rejected. The rest of the time (eg. 5% or 1%) even when the null hypothesis is true this threshold will be exceeded, and so it may be falsely rejected.

Well, I could go on about this for hours, but I think we have gone way farther than the questioner really needed.

Any chance someone can explain null hypothesis?
If my hypothesis is that age affects physical activity – after completing a random survey and this point is not proved – is this my null hypothesis? Then I wouldn’t reject it, so what do I do?
Post your problem. That would be best.