We have seen till now that what is hypothesis testing.
How we create different null and alternate hypotheses but how many types of hypothesis tests are present that we will be covering in this module.
This module will be very important.
Why? because by using different types of tests how we can convert any pharmaceutical use cases or any industry use case into any hypothesis testing example.
That we'll be seeing in this module.
So, we have many different types of tests like the very first is the Z test, we perform the Z test by two ways.
One is critical value method and one is p value method.
We will be covering them in detail in the coming modules.
Next, we will cover the T-test, the t test is simply our…first we will see what is the distribution individually by using different samples we can perform one sample mean test, if I have paired population, we perform two sample mean test if I have one unpaired or two sample proportion test.
What are these? Where all we can use them.
Do we use them in continuous variable or categorical variable? All these things we will see in detail.
After that the very important testing of T-test is the AB testing, which is used by many companies like Google and Amazon, before any of their feature launch.
So, how do they work? With all these examples, we will cover it in the next module.
Next there is also a chi-square test, even this is performed in two ways.
One is independence test and second is goodness of fit, we will see them both in detail next.
One which is an very important test, which we call as F test or ANOVA test ANOVA test can also be performed in one way ANOVA or two way ANOVA.
How all these different tests that are performed, which test do we use in which type of scenario that we will see in the coming chapters.
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This course is really nice, just have one question in empirical rule explanation , SD deviation example trainer is saying mean however mean (20+30+40+50+60+70/6) value is different kindly confirm than