Take, for example, the investigation of a fugitive from justice. The null hypothesis states that the individual is innocent, while the alternative states that they are guilty. In this situation, a Type I error would result in the individual being found guilty and sentenced to prison, despite the fact that he or she is innocent.
A type I error happens when a null hypothesis that is otherwise right is rejected in statistical hypothesis testing, whereas a type II error occurs when the null hypothesis is not rejected even though it is false.
The risk of mistakenly failing to reject the null hypothesis when it is not applicable to the entire population is known as a type II error. In essence, a type II error is a false negative.
A type I error (false-positive) occurs when an investigator rejects a null hypothesis that is true in the population, while a type II error (false-negative) occurs when an investigator fails to reject a null hypothesis that is true in the population.
Increase the number of people in the sample. Increasing the sample size used in a test is one of the simplest ways to improve the test's power.
Increase the level of relevance. Another option is to select a greater relevance level.
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Akash Sambhaji
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plz provides all notes
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Jamil Akhtar
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plzz provide notes....
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Sunita Singhal
5
please provide notes also in pdf
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Montu Mali
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nice ☺️👍
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Abdul Samed
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please provide course notes
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Shashi Kumar
5
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
5
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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