http://wiki.answers.com/Q/Null_hypotheses_on_5_basketball_players_jump_shots"
null hypotheses and alternative hypotheses
The Players of Null-A was created in 1956.
Scientific research does require the formulation and testing of hypotheses of various kinds.
The null and alternative hypotheses are not calculated. They should be determined before any data analyses are carried out.
thanks for your response! teacher4life
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.
To start with you select your hypothesis and its opposite: the null and alternative hypotheses. You select a confidence level (alpha %), which is the probability that your testing procedure rejects the null hypothesis when, if fact, it is true.Next you select a test statistic and calculate its probability distribution under the two hypotheses. You then find the possible values of the test statistic which, if the null hypothesis were true, would only occur alpha % of the times. This is called the critical region.Carry out the trial and collect data. Calculate the value of the test statistic. If it lies in the critical region then you reject the null hypothesis and go with the alternative hypothesis. If the test statistic does not lie in the critical region then you have no evidence to reject the null hypothesis.
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.
You mean SQL? NULL = anything IS NULL NULL <> anything IS NULL ... NULL IS NULL = TRUE NULL IS NOT NULL = FALSE
The significance level is always small because significance levels tell you if you can reject the null-hypothesis or if you cannot reject the null-hypothesis in a hypothesis test. The thought behind this is that if your p-value, or the probability of getting a value at least as extreme as the one observed, is smaller than the significance level, then the null hypothesis can be rejected. If the significance level was larger, then statisticians would reject the accuracy of hypotheses without proper reason.
There is no null, it is just what it says when you log out. There is a null.
An example of a null hypothesis could be "There is no significant difference in test scores between students who received tutoring and those who did not receive tutoring." This hypothesis suggests that any observed difference in test scores is due to random chance rather than the tutoring intervention.