 इजम्ट्टन अप अप आप पोसे टेस्टिंगे भाद करेंगे start में ज़ह भी हम कोई टेस्टिस दिश लिए तो आप उस देखे अप आप उजट्ट्टन्स देखेंगे। of each individual to be selected in the sample. Known and equal chances are there. So, for the Z test, particularly our random sampling, after that our independent observation assumption is that whatever sample you have taken, every sample should be independent, i.e. it should not depend on each other. Thirdly, our assumption should be mutually exclusive. i.e. as we are doing first, now we have discussed the null hypothesis and alternative hypothesis. So, these two events are mutually exclusive. The meaning of mutually exclusive is that either one can happen, they both cannot occur at the same time. They cannot occur together. Either your null hypothesis will be rejected or your research hypothesis will be rejected. Thirdly, the value of standard deviation is unchanged by the treatment. i.e. if you draw a sample from an unknown population, and apply a treatment on that sample, i.e. I have given a training to the students that after the training, how many performances or learning have been changed. i.e. our standard deviation is basically variability. We have read that how much every value is deviating from its means. So, the actual sample deviation in the population will basically remain the same after the treatment. So, we assume that the standard deviation for the unknown population after the treatment is the same as it was for the population before the treatment. i.e. first, you have 100 rupees before the treatment or some people have 100 rupees, some have 150 rupees, some have 200 rupees. I have given 50-50 rupees to all of them. Now, their deviation will remain the same. i.e. those who have 100 rupees have 150 rupees, those who have 150 rupees have 200 rupees, those who have 200 rupees have 250 rupees. So, the first deviation in the population will remain the same. So, this is also assumption. When we will test this, I will tell you in this basis how we assume it. The fourth assumption is the normal sampling distribution. The normal sampling distribution means that for hypothesis with the z-score, the unit normal table is used to identify the critical region. Similarly, if you have a z-test, a t-test, then you will find the values of the same table because they are standard distributions. And accordingly, you will find the critical values of the same table and alpha.