Okay. So, for inferential statistics, these are hypotheses. So, hypothesis can be now hypotheses and can also be alternate hypotheses. So we can right now hypothesis and alternative hypothesis as follows. So, for now hypothesis we can write the null hypothesis is the mean or I say data one is equal to the mean on data two far the alternate hypothesis we can write the mean of the top one is not equal to the mean of the top two. So, this one is the mean of one data and then this is the menaul and other data So for inferential statistics, we can use auto saw statistical test to get our P value we can use our T test for continuous variables.
And we can use our T square test for categorical variables. And we can use Hannover for let's say, ah for more verbose or more compressed testing. So, a small p value E, sa, P value is less than or equal to alpha. Where these are alpha is usually 0.05 e let's say the p value is less than 0.05. We can see that our data is sufficiently inconsistent return now hypotheses. Hands are now hypothesis Maybe rejected.
If the p value is larger than 0.05 then we say that we fail to reject a null hypothesis. So let's say our no hypothesis is that the mean or the first time the mean of the second data is the same. So let's say we put in putting the data or let's say we use the statistical test. And then when we get a p value that is less than 0.05. Then we can say that, now hypothesis may be rejected. So we can say that we can reject this null hypothesis.
Then we can say that ah, alternate hypothesis. mean for the first data is not equal to the mean for the second data. So let's say we get a p value that is larger than 0.05 then we fail to reject a null hypothesis. So, we can say that a mean or the first data is equal to the mean of the second data. So for inferential statistics, we actually are conduct adisa statistical testing to see whether the mean of two data is the same all the mean or se two data is different. So we can conduct all this testing to see whether our hypothesis is alternate hypothesis or the null hypothesis.
So this is Ravi the inferential statistic. So for inferential statistics today we look into t test. We are looking to try square test and test