Means are not all equal. In this example, the hypotheses are:
Specifically, the four steps involved in using the P-value approach to conducting any hypothesis test are: Specify the null and alternative hypotheses.
Using the sample data and assuming the null hypothesis is true, calculate the value of the test statistic. Using the known distribution of the test statistic, calculate the P-value: Right Tailed The P-value for conducting the right-tailed test H0: Recall that probability equals the area under the probability curve.
It can be shown using statistical software that the P-value is 0. The graph depicts this visually. Therefore, our initial assumption that the null hypothesis is true must be incorrect. That is, since the P-value, 0. Note that we would not reject H0: The P-value for conducting the left-tailed test H0: The P-value for conducting the two-tailed test H0: That is, the two-tailed test requires taking into account the possibility that the test statistic could fall into either tail and hence the name "two-tailed" test.
Note that the P-value for a two-tailed test is always two times the P-value for either of the one-tailed tests. Now that we have reviewed the critical value and P-value approach procedures for each of three possible hypotheses, let's look at three new examples — one of a right-tailed test, one of a left-tailed test, and one of a two-tailed test.
The good news is that, whenever possible, we will take advantage of the test statistics and P-values reported in statistical software, such as Minitab, to conduct our hypothesis tests in this course.Statistical Hypothesis Testing The formal statistical procedure for performing a hypothesis test is to state two hypotheses and to use an appropriate statistical test to reject one of the hypotheses and therefore accept (or fail to reject) the other.
Business Applications of Hypothesis Testing and Confidence Interval Estimation from Rice University.
Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. A mastery over these topics will help Basic Info: Course 3 of 5 in the Business Statistics and Analysis Specialization.
hypothesis testing So far we have learned how to take raw data, combine it, and create statistics that allow us to describe the data in a brief summary form. We have used statistics to describe our samples. Bayesian hypothesis testing is a subjective view of the same thing. It takes into account how much faith you have in your results.
In other words, would you wager money on the outcome of your experiment? Differences Between Traditional and Bayesian Hypothesis Testing. The hypothesis is based on available information and the investigator's belief about the population parameters. The specific test considered here is called analysis of variance (ANOVA) and is a test of hypothesis that is appropriate to compare means of a continuous variable in two or more independent comparison groups.
Statistical hypothesis testing is a key technique of both frequentist inference and Bayesian inference, although the two types of inference have notable differences. Statistical hypothesis tests define a procedure that controls (fixes) the probability of incorrectly deciding that a default position (null hypothesis) is incorrect.