0
Statistical tests compare the observed (or more extreme) values against what would be expected if the null hypothesis were true. If the probability of the observation is high you would retain the null hypothesis, if the probability is low you reject the null hypothesis. The thresholds for high or low probability are usually set arbitrarily at 5%, 1% etc.
Strictly speaking, when rejecting the null hypothesis, you do not accept the alternative hypothesis because it is possible that neither are true and it is the model itself that is wrong.
Add your answer:
Earn +20 pts
What is power function in statistics?
In statistics, we have to test the hypothesis i.e., null hypothesis and alternative hypothesis. In testing, most of the time we reject the null hypothesis, then using this power function result, then tell what is the probability to reject null hypothesis...
What is Hypothesis Testing of Type II Error?
Failing to reject a false null hypothesis.
Differences between null and alternative hypothesis?
The difference between the null hypothesis and the alternative hypothesis are on the sense of the tests. In statistical inference, the null hypothesis should be in a positive sense such in a sense, you are testing a hypothesis you are probably sure of. In other words, the null hypothesis must be the hypothesis you are almost sure of. Just an important note, that when you are doing a tests, you are testing if a certain event probably occurs at certain level of significance. The alternative hypothesis is the opposite one.
What is the meaning of hypothesis in statistics?
A hypothesis is the first step in running a statistical test (t-test, chi-square test, etc.) A NULL HYPOTHESIS is the probability that what you are testing does NOT occur. An ALTERNATIVE HYPOTHESIS is the probability that what you are testing DOES occur.
In a hypothesis testing the alternative hypothesis is assumed?
No. The null hypothesis is assumed to be correct unless there is sufficient evidence from the sample and the given criteria (significance level) to reject it.
Is the null hypothesis considered correct until proven otherwise?
No. The null hypothesis is not considered correct. It is an assumption, and hypothesis testing is a consistent meand of determining whether the data is sufficiently strong to say that it may be untrue. The data either supports the alternative hypothesis or it fails to reject it. See examples in links. Also note this quote from Wikipedia: "Statistical hypothesis testing is used to make a decision about whether the data contradicts the null hypothesis: this is called significance testing. A null hypothesis is never proven by such methods, as the absence of evidence against the null hypothesis does not establish it."
What is Hypothesis Testing of Type I Error?
Rejecting a true null hypothesis.
Explain what is meant by null hypothesis?
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.
What is Hypothesis Testing of Power?
The probability of correctly detecting a false null hypothesis.
What is power function in statistics?
In statistics, we have to test the hypothesis i.e., null hypothesis and alternative hypothesis. In testing, most of the time we reject the null hypothesis, then using this power function result, then tell what is the probability to reject null hypothesis...
What is Hypothesis Testing of Type II Error?
Failing to reject a false null hypothesis.
What is Hypothesis Testing of Beta Value?
Probability of failing to reject a false null hypothesis.
Can Bayesian testing come to the same conclusion as null hypothesis significance testing?
Yes, it can.
What is the Probabilities of Beta Value?
In hypothesis testing, this is the probability of failing to reject a false null hypothesis.
Differences between null and alternative hypothesis?
The difference between the null hypothesis and the alternative hypothesis are on the sense of the tests. In statistical inference, the null hypothesis should be in a positive sense such in a sense, you are testing a hypothesis you are probably sure of. In other words, the null hypothesis must be the hypothesis you are almost sure of. Just an important note, that when you are doing a tests, you are testing if a certain event probably occurs at certain level of significance. The alternative hypothesis is the opposite one.
If we reject the null hypothesis what can we conclude about the alpha risk?
If we reject the null hypothesis, we conclude that the alternative hypothesis which is the alpha risk is true. The null hypothesis is used in statistics.
What are the advantages of null hypothesis significance testing?
The add vantage of the null hypothesis testing is when used to determine errors in tolerances the null hypothesis will always land you on the side of safety. This is often used when the importance of quantity or quality is extremely important. The case of filling medicine in to a capsule would require a very close tolerance, the null hypothesise could set an undefined error, however this is much safer than a wide tolerance which could result in a dangerous outcome.
