Now that we have seen the concept of population was a sample see hypothesis testing.
Hypothesis testing validates the inference we have drawn for the population based on our study of the
sample.
In this example, the application of fertilisers leads to plant growth, so the baby will still be hypothesises.
Application of fertilizer increases, planned growth.
This is what we are trying to prove and what we are trying to prove is mentioned as an alternate hypothesis.
And the opposite of this is what is known as null hypothesis.
We don't prove a hypothesis directly.
Rather, we either accept or reject the alternate hypothesis.
That's how we go about hypothesis testing.
Right.
Even though hypothesis tests are meant to be reliable, they are not 100 percent certain.
Right.
You will have what are known as type one time to error upon error is known as false positive.
Type two errors, false negative.
An old man being diagnosed as pregnant or a pregnant lady not being diagnosed as pregnant are obviously
errors.
Right.
But you will have these kind of errors in real life.
A test with 95 percent confidence level because in hypothesis testing, we go for confidence level,
that is what is the confidence with which I am drawing the inference.
OK, so when you say that a particular test has got a 95 percent confidence level, it means that is
a five percent chance of getting a type one error, you know, which is a false positive scenario.
So the key point to remember is.
You will have errors in real life scenario.
Our aim should be to reduce the occurrence of these errors.
You cannot prevent them.
But you can reduce the occurrence of these areas, one of the best ways to reduce the occurrence of
these errors is ensuring randomness and representativeness of the sample.
OK.