A type I error happens when a correct null hypothesis is rejected in statistical analysis, but a type II error occurs when a null hypothesis that is actually untrue is not rejected. Even though the error does not happen by chance, it rejects the alternative hypothesis.
A type II error occurs when the null hypothesis is wrong and you fail to reject it. The likelihood of making a type II error is β, which is dependent on the test's power.
In the context of hypothesis tests, there are two sorts of errors: type I and type II. When a true null hypothesis is rejected (a "false positive"), a type I error occurs, and when a false null hypothesis is not rejected (a "false negative"), a type II error occurs.
The null hypothesis is rejected. H0 is a strong statement indicating that H0 does not appear to be compatible with the data. H0 is not rejected, which is a weak statement that should be read to suggest that H0 is compatible with the evidence.
There is significant statistical evidence that the null hypothesis is correct if the null hypothesis is not rejected. Failure to reject a faulty null hypothesis is a type II error.
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plz provides all notes
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plzz provide notes....
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please provide notes also in pdf
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nice ☺️👍
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please provide course notes
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Shashi Kumar
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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Nikhil Fapale
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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