The content covered in this section includes; Hypothesis Testing, T-Tests, Cross-Tabulations, and Chi-Square test. The difference between a null and alternative hypothesis and how to set up the hypothesis (the specific symbolic components that are often used in testing hypotheses) will be covered in detail. Student will have the opportunity to explore the difference between one- and two-tailed testing and understand the importance of the P-value for setting up proper hypothesis testing. Type I and Type II errors should be understood in the context of hypothesis testing and how increasing or decreasing the level of rejection can lead to greater levels of certain types of errors. The second part of this section is an introduction to cross-tabulations, Chi-Square test, t-tests, and One-Way ANOVA parametric and nonparametric measures. Importance is placed on calculating these tests through the use of statistical software, as well as analyzing and interpreting their results. In addition to manipulating computations, the conceptual basis of each test will be explored. (also called significance testing) is a statistical procedure for discriminating between two statistical hypotheses; the null hypothesis (H ) and the alternative hypothesis ( H , often denoted as H ). Hypothesis testing rests on the presumption of validity of the null hypothesis; the null hypothesis is rejected only if the data at hand testify strongly enough against it. The philosophical basis for hypothesis testing rests in t
he fact that random variation pervades all aspects of life and in the desire to avoid being fooled by what might be chance variation. A is a statistical hypothesis test based on a test statistic whose sampling distribution is a t-distribution. Various t-tests are aimed at testing hypotheses about populations with normal probability distribution. However, t-tests often provide adequate results for non-normally distributed populations. The most popular t-tests are aimed at testing the following hypotheses: 1) The population mean is as hypothesized (the population variance is not known). 2) The means of two populations are equal (the population variances are not known but equal). 3) The means of two populations are equal (the population variances are not known and not equal). 4) The correlation coefficient for two random variables is zero. 5) The slope of the population regression line is zero. 1. Read Chapter 9 in the following primary course resource: (a) Fundamentals of Hypothesis Testing (b) Hypothesis Tests for Population Means (c) Hypotheses Tests for Population Proportions (a) Clearly identify the objective of your test. (b) Clearly identify what the conditions, your hypotheses, and your rejection criteria. (c) Determine the appropriate sample sizes and provide justification for the size. (d) Analyze the data and share an appropriate descriptive statistics of your samples. (e) Calculate test the test statistics and rejection criteria. (f) Clearly share your conclusion and opinion.