00:01
In hypothesis testing, you can say two types of testing are there.
00:11
So, here you can say type 1 error occurs when null hypothesis is incorrectly rejected.
00:23
And type 2 error is occurring when the null hypothesis is incorrectly accepted.
00:32
This is rejected, incorrectly rejected and incorrectly accepted.
00:38
So, how you will find it? let us just see in this situation.
00:44
So, the consumer group conducted a hypothesis test to assess the manufacturer's claim that the laptop batteries can be recharged on average 500 times.
00:58
Right.
01:01
So, the resulting p -value is 0 .1111 which is greater than the conventional significance level which is 0 .05.
01:11
Right.
01:12
Assuming a significance number level is 5 percent.
01:16
According to that, 0 .1111 is very large there.
01:19
Right.
01:20
So, since the null hypothesis is not rejected based on the p -value.
01:25
Right.
01:26
So, it is independent of it.
01:28
So, the consumer group concludes that the manufacturer's claim is accurate.
01:37
Right.
01:37
However, it is important to note that the null hypothesis being accurate does not guarantee that the claim is true.
01:46
Right.
01:46
It is simply mean, it simply means that the, that there is not enough evidence to reject the null hypothesis.
01:57
Right.
01:57
So, in case, in this case, the null hypothesis is that the average number of charges is 500.
02:06
Right.
02:07
So, the possible errors could be in two ways.
02:11
First, type 1 error as i already like discussed, type 1 and type 2 error type...