Do the null hypothesis and alternate hypothesis have to be exhaustive?
The logic of traditional hypothesis testing requires that we set up two competing statements or hypotheses referred to as the null hypothesis and the alternative hypothesis. These hypotheses are mutually exclusive and exhaustive. The null hypothesis is then assumed to be true unless we find evidence to the contrary.
Is alternative hypothesis mutually exclusive?
The alternative hypothesis is one of two mutually exclusive hypotheses in a hypothesis test. The alternative hypothesis states that a population parameter does not equal a specified value. Typically, this value is the null hypothesis value associated with no effect, such as zero.
Why null and alternative hypothesis should always be mutually exclusive?
The null hypothesis, H0, is the opposite of what you are hoping to claim. It is extremely important that the alternative hypothesis and null hypothesis be mutually exclusive, meaning that if one is true, the other must be false.
What is a alternative hypothesis example?
The alternate hypothesis is just an alternative to the null. For example, if your null is “I’m going to win up to $1,000” then your alternate is “I’m going to win $1,000 or more.” Basically, you’re looking at whether there’s enough change (with the alternate hypothesis) to be able to reject the null hypothesis.
Why do null and alternative hypotheses have to be exhaustive?
In such a case, exhaustivity is impossible, as the alternative would then have to cover all possible probability models. The main reason you see the requirement that hypotheses be exhaustive is the problem of what happens if the true parameter value is in the region which is not covered by either the null or alternative hypothesis.
How is the null statement used in hypothesis testing?
The null statement must always contain some form of equality (=, ≤ or ≥) Always write the alternative hypothesis, typically denoted with Ha or H1, using less than, greater than, or not equals symbols, i.e., (≠, >, or <). If we reject the null hypothesis, then we can assume there is enough evidence to support the alternative hypothesis.
Do you have to have an alternative hypothesis?
Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted. The alternative does not need to be exhaustive neither the null hypothesis must necessarily mean something different than what it states.
Is there a reason for hypotheses to be exhaustive?
Could someone more experienced explain which is true, and I would be grateful for shedding some light on the (historical?) reasons for such difference (the books were written by statisticians after all, i.e. scientists, not philosophers). On principle, there is no reason for hypotheses to be exhaustive.