A prediction or statement about a characteristic of a variable, which can be tested to provide evidence for or against.
The null hypothesis assumes randomness and is directly tested during a significance test. As the null hypothesis indicates no significance, you are usually trying to disprove the null hypothesis in your statistical tests.
Contradicts against the null hypothesis, the alternative hypothesis is supported if the significance test indicates the null hypothesis to be incorrect.
Choosing the correct hypothesis test
Firstly, you should define the objective of your hypothesis test and then consider the most valid type of hypothesis test. Much of the decision-making process is determined by the number of populations you have to analyse and whether or not the variances are known:
Comparing a sample against a target:
Comparing two samples against each other:
Comparing more than two samples:
Sometimes you will not be testing whether a hypothesis is true or false but estimating how big the effect is, in these examples measuring a confidence interval may be more appropriate.
Combining information from multiple sources to help arrive at the most accurate conclusion possible, often by testing the same hypothesis using numerous different methods.