is this statement true?
Hypothesis tests are dsigned to measure the amount of evidence that exists against the alternative hypothesis?
Not in my experience. My experience is that hypothesis tests are designed to see if the hypothesis is true within some predetermined confidence measure.
Typically tests are used to see if there is evidence to contradict the null hypothesis (that there are no real differences) rather than an alternate hypothesis.
I hope this helps a little more. Thanks for asking.
I don't know if this article will be helpful or not. However, it seems to relate tangentially to your question.
http://www.members.cox.net/dagershaw/lol/Prove.Point.htm
Again, I hope it halps a little more. Thanks for asking.
The statement you provided is not completely accurate. Hypothesis tests are designed to evaluate the strength of evidence against the null hypothesis, not the alternative hypothesis. The null hypothesis typically states that there is no effect or difference between the variables being studied, while the alternative hypothesis states the opposite.
To explain how to determine if a statement is true or not, we need to consider the purpose of hypothesis testing. Hypothesis testing is a statistical method that allows us to make inferences about a population based on a sample of data. It involves formulating a null hypothesis and an alternative hypothesis, collecting data, and using statistical techniques to determine whether the evidence in the data supports the null hypothesis or provides enough evidence to reject it and support the alternative hypothesis.
To determine if the statement is true or not, we can consult reliable sources such as textbooks, academic papers, or trusted statistical websites. These sources will provide accurate information about the purpose and design of hypothesis tests.
In this case, we can conclude that the statement is not entirely true based on the commonly understood purpose of hypothesis testing. The primary aim of hypothesis testing is to evaluate the strength of evidence against the null hypothesis, rather than the alternative hypothesis.