Now, let's get further deeper into hypothesis testing, so while doing hypothesis testing, we might
come at two types of errors.
The two types of errors are five one error, and they do it.
Here you can see a truth table, so here we have witch's brew to about the population.
That this one could be that the hypothesis null hypothesis vagi, which we have figured out is true,
and another would be that the alternate hypothesis could be true.
Now, based on the samples, we can make two types of decisions.
One is to reject the null hypothesis and another would be to accept the null hypothesis.
Now, in case the truth about the population is that the null hypothesis is true, but based on the
data, we somehow reject the null hypothesis.
Then this is called type one errors.
And another type of error is when the truth about the population is that the alternate hypothesis is
true by the.
Accept the null hypothesis based on the samples given, so when we accept the null hypothesis, when
actually all the data but this is was true, that is called by the data and when the null hypothesis
is true, but we somehow magically it is called the type one.
Never.
Some example of the type one error could be one example where a male human is tested positive for being
pregnant.
Now, this scenario is not really possible.
So formally it is defined as the incorrect rejection of a null hypothesis.
So the null hypothesis in this case will be that a male human is not pregnant.
So the hypothesis is that male human is not pregnant and this hypothesis is true.
The hypothesis is completely true that a human cannot be pregnant, but based on the data, reject this
null hypothesis, which is type one error.
The other type of error is when null hypothesis is that a human is pregnant, a male human is pregnant,
and the best support the null hypothesis.
So when we are saying that a male human is pregnant and the test suggests that this is true and it kind
of allows us to accept the null hypothesis.
So this is called tied Twitter because here they put the hypothesis is not correct.
The null hypothesis is not true in this condition.
So it is defined as the acceptance of the false hypothesis.
So these are the two types of errors which we can commit by performing hypothesis testing.
Now, while we were discussing about hypothesis testing, we were continuously talking about the statistical
significance.
So what is the statistical significance?
The p value is how likely it was that our sample was drawn from a hypothetical population where nothing
was going on.
So it tells us, like how likely it is that the population is where nothing was going on, so that the
statistical significance simply means that the all being preserved are unlikely to be present, a situation
where there was no relationship between the.
So.
The differences are big enough to unlikely to have happened simply due to jobs.
So we want to make sure that the two means which we have achieved are actually not because of John's.
So we want to find out if there is a chance that they are just because of what John saw different,
or is it actually a part of two different population, which is the reason why there is so much difference
between them?
So if the values are significantly Partovi, if the values are far obvious or that one value is in the
central area and another value is found in the fields of the gold or in the critical region or below
or above the region, then it is going to be significantly different.
The smaller the P value, the greater your confidence in the statistical result.
Now, the smaller the probability value, the smaller the probability value of the the value being part
of the same distribution, the more is the chance that it is not a part of the same distribution, but
actually a part of a different distribution.
Now, the fight does not change, whereas the P-value are dependent on the actual value of those statistics
and the question so the value is just the line which we have drawn, it is just a constraint which we
have given that we have won to have ninety five percent level or ninety nine percent level or 90 percent
level.
If value will not change by the probability of the value which we are actually trying to find out on
the namiko, that value will keep on changing.
That is something which we are trying to find out if it is significantly far away from this original
value or is it not part of a belongs to the same namiko?
In the next session, we will work on this and we will try to understand what the best is and how we
can conduct this and what are the different types of best.