type 1 error is error when one rejects a true null hypothesis-the alpha value called the level of significance is the probability of rejecting the true null hypothesis.

Type 2 error is error when one fails to reject a false null hypothesis- the beta value is the probability of failing to reject the false null hypothesis.

I agree to what Prof Fadhil has stated hereinabove

Regards

My Dear ,

A type I error (or error of the first kind) is the incorrect rejection of a true null hypothesis. Usually a type I error leads one to conclude that a supposed effect or relationship exists when in fact it doesn't. Examples of type I errors include a test that shows a patient to have a disease when in fact the patient does not have the disease, a fire alarm going on indicating a fire when in fact there is no fire, or an experiment indicating that a medical treatment should cure a disease when in fact it does not.

A type II error (or error of the second kind) is the failure to reject a false null hypothesis. Examples of type II errors would be a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease; a fire breaking out and the fire alarm does not ring; or a clinical trial of a medical treatment failing to show that the treatment works when really it does

Best Regards

type 1 error is error when one rejects a true null hypothesis-the alpha value called the level of significance is the probability of rejecting the true null hypothesis.

Type 2 error is error when one fails to reject a false null hypothesis- the beta value is the probability of failing to reject the false null hypothesis.

I agree to what Prof Fadhil has stated hereinabove

Regards

Type-1 error is also known as a "false positive" where you claim there was an effect but there was none (i.e., you reject the null hypothesis when you should not have)

Type-2 error is also known as a "false negative" where you claim there was no effect but there was one (i.e., you accept the null hypothesis when you should not have).

In testing each statistical hypothesis, two type of errors may occur which are type I and II errors. Type I error is the probability of rejecting a true hypothesis and type II error is the probability of accepting a false hypothesis.

All first 4 answers are satisfactory

I support all the previous response. Type 1 Error and Type 2 Error have been explained well in the previous.