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What is the null hypothesis of a one way ANOVA What is the alternate hypothesis?
Wiki User
∙ 12y ago
When writing hypotheses the null hypothesis is generally the hypothesis stating that there will be no significant difference between the variables you are testing. An alternate hypothesis would be a hypothesis suggesting that the results will be anything other than not significant. For example if you were testing three concentrations (low, medium, and high) of a type of medication on cancer cells, then one example of an alternate hypothesis would be that the medium concentration would decrease the number of viable cancer cells.
Wiki User
∙ 12y ago
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Why have a null hypothesis?
Statistical tests are designed to test one hypothesis against another. Conventionally, the default hypothesis is that the results were obtained purely by chance and that there is no observed effect acting on the observations - ie the effect is null. The alternative is that there IS an effect.
What is the meaning of null hypothesis?
In statistics, a null hypothesis is the hypothesis which you wish to test against some alternative. Often, it is framed in a way that is the opposite of what you wish to prove. You then collect the data and, if the resulting test statistic is such that observations which are at least as extreme as the one realised is very unlikely under the null hypothesis, then it is rejected and the alternative accepted.
Kalvin tosses a coin five days in a row and gets tails every time Do you think there is something wrong with the coin How can you find out?
There is a 1/16 probability that 5 tosses end with the same result - 1/32 that they are all tails. In this kind of example, most statisticians would not reject the hypothesis of a fair coin unless the probability was less than 5% or 1/20. The null hypothesis is that the coin is a fair coin. If the alternative hypothesis is that something is wrong with the coin, the probability of a result such as the one observed (and its mirror image) is 6.25%. So you would not reject the null hypothesis at the 5% level. However, if your alternative is that the coin favours tails, the probability of as extreme an outcome is 0.03125 or 3.125% and you would reject the null hypothesis. This is a marginal case at the 5% level and you may wish to toss the coin a few more times to reduce the probability of the outcome occurring purely by chance.
What is a statement of how variables are expected to be related to one another?
Hypothesis
Why empty set or null set is subset of every set?
There is only one empty set, also known as the null set. It is the set having no members at all. It is a subset of every set, since it has no member that is not a member of any other set.
What is the null hypothesis of a one-way ANOVA What is the alternate hypothesis?
Null hypothesis of a one-way ANOVA is that the means are equal. Alternate hypothesis a one-way ANOVA is that at least one of the means are different.
In a two-way ANOVA you can test main hypothesis and one interactive hypothesis?
Not sure about an interactive hypothesis: are you sure you don't mean alternative hypothesis?
Difference between acceptance and rejection region?
Some researchers say that a hypothesis test can have one of two outcomes: you accept the null hypothesis or you reject the null hypothesis. Many statisticians, however, take issue with the notion of "accepting the null hypothesis." Instead, they say: you reject the null hypothesis or you fail to reject the null hypothesis. Why the distinction between "acceptance" and "failure to reject?" Acceptance implies that the null hypothesis is true. Failure to reject implies that the data are not sufficiently persuasive for us to prefer the alternative hypothesis over the 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.
Why have a null hypothesis?
Statistical tests are designed to test one hypothesis against another. Conventionally, the default hypothesis is that the results were obtained purely by chance and that there is no observed effect acting on the observations - ie the effect is null. The alternative is that there IS an effect.
Types of Hypothesis and definition?
In formal design and analysis of experiments there are but two types of hypotheses: null and alternative. And one might argue there really is only one because when the null is properly defined, the alternative is automatically properly defined. The null hypothesis is a testable statement of conjecture. The purpose of the null hypothesis is to set the measurable goal for the experiment that follows to show that the null is not false. If the results of the experiment do not show that then the alternative hypothesis is by definition not false. Simple Example: Null: It's raining outside. Alt: It's NOT raining outside. NOTE: The NOT reverses the logic of the null. The experiment...walk outside. The test...if I get wet, the Null is not false. If I don't get wet, the alternative is not false. NOTE: I must have an experiment to test the hypothesis. Without a test it's not a valid hypothesis.
In what cases one can state the null hypotheses true?
There is no truth in science. Truth is only meaningful in math, philosophy, religion and logic. A hypothesis can never be true. You either accept or reject a hypothesis. You accept the null hypothesis if you fail to reject it.
What is the difference between the null hypothesis and an alternative hypothesis?
In statistics the null hypothesis is usually the one that asserts that the data come from some defined distribution. The alternative hypotheses may simply be that they do not, or it may be that they come from some other, defined distribution.
What does a p value of 0.66 tell us?
A p-value is the probability of obtaining a test statistic as extreme or more extreme than the one actually obtained if the null hypothesis were true. If this p-value is less than the level of significance (usually set by the experimenter as .05 or .01), we reject the null hypothesis. Otherwise, we retain the null hypothesis. Therefore, a p-value of 0.66 tell us not to reject the null hypothesis.
Why is an ANOVA test one-tailed?
The short answer is ANOVA is not one-tailed.
Are F-test and ANOVA the same in statistics?
The F-test is designed to test if two population variances are equal. It compares the ratio of two variances. If the variances are equal, the ratio of the variances will be 1.The F-test provides the basis for ANOVA which can compare two or more groups.One-way (or one-factor) ANOVA: Tests the hypothesis that means from two or more samples are equal.Two-way (or two-factor) ANOVA: Simultaneously tests the hypothesis that the means of two variables from two or more groups are equal.
What is the meaning of null hypothesis?
In statistics, a null hypothesis is the hypothesis which you wish to test against some alternative. Often, it is framed in a way that is the opposite of what you wish to prove. You then collect the data and, if the resulting test statistic is such that observations which are at least as extreme as the one realised is very unlikely under the null hypothesis, then it is rejected and the alternative accepted.
