The concept we just discussed in the preceding section is used for a very important technique in statistics, called hypothesis testing. In hypothesis testing, we assume a hypothesis (generally related to the value of the estimator) called null hypothesis and try to see whether it holds true or not by applying the rules of a normal distribution. We have another hypothesis called alternate hypothesis.
There is a catch in deciding what will be the null hypothesis and what will be the alternate hypothesis. The null hypothesis is the initial premise or something that we assume to be true as yet. The alternate hypothesis is something we aren't sure about and are proposing as an alternate premise (almost often contradictory to the null hypothesis) which might or might not be true.
So, when someone is doing a quantitative research to calibrate the value of an estimator, the known value of the parameter is taken as the null hypothesis while the new...
