the hypothesis might be correct
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The available evidence suggests that the observations were less likely to have been obtained from random variables that were distributed according to the null hypothesis than under the alternative hypothesis against which the null was tested.
You may want to prove that a given statistic of a population has a given value. This is the null hypothesis. For this you take a sample from the population and measure the statistic of the sample. If the result has a small probability of being (say p = .025) if the null hypothesis is correct, then the null hypothesis is rejected (for p = .025) in favor of an alternative hypothesis. This can be simply that the null hypothesis is incorrect.
The hypothesis test.
The null hypothesis is an hypothesis about some population parameter. The goal of hypothesis testing is to check the viability of the null hypothesis in the light of experimental data. Based on the data, the null hypothesis either will or will not be rejected as a viable possibility.
We have two types of hypothesis i.e., Null Hypothesis and Alternative Hypothesis. we take null hypothesis as the same statement given in the problem. Alternative hypothesis is the statement that is complementary to null hypothesis. When our calculated value is less than the tabulated value, we accept null hypothesis otherwise we reject null hypothesis.
no
There are two types of errors associated with hypothesis testing. Type I error occurs when the null hypothesis is rejected when it is true. Type II error occurs when the null hypothesis is not rejected when it is false. H0 is referred to as the null hypothesis and Ha (or H1) is referred to as the alternative hypothesis.
In hypothesis testing, a Type I error occurs when a true null hypothesis is incorrectly rejected, while a Type II error occurs when a false null hypothesis is not rejected.
It tells us that H1,H0 (alternative )hypothesis is selected
Usually when the test statistic is in the critical region.
it is called structural resources because it has null as word
It means that, if the null hypothesis is true, there is still a 1% chance that the outcome is so extreme that the null hypothesis is rejected.
Yes.