Posted by Angela on Thursday, June 10, 2010 at 8:44pm.
When setting up a statistical test, two hypotheses are formed: a null hypothesis and an alternate hypothesis. The null hypothesis tests the probability that the sample mean (taken from sample data) is very likely drawn from the population mean. If there is a low probability that the sample mean has been drawn from the population mean, the null will be rejected and the alternate will be accepted. The significance level (usually stated in a problem) is the probability at which the null will be rejected and the alternate accepted. For example, if a significance level is set at .05, there is a 5% probability that the null will be rejected. Often tables are used for values that correspond to the level stated. These values are known as critical or cutoff values; they cut off the distribution at that point. If an observed value (calculated from a formula) exceeds a critical value from a table, then the null will be rejected. If the observed value does not exceed the critical value from a table, then the null will not be rejected. There are many different kinds of tables depending on the type of test. A one-tailed test shows a difference in a particular direction. A two-tailed test is just looking for a difference in either direction.
I hope these few hints will help you determine your answers.
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