Hypothesis+Testing

Outline – Hypothesis Testing
A statistical hypothesis test is a method of making statistical decisions using experimental data. It is sometimes called confirmatory data analysis. There are two types of statistical inferences: estimation of population parameters and hypothesis testing. Hypothesis testing is one of the most important tools of application of statistics to real life problems. Most often, decisions are required to be made concerning populations on the basis of sample information. Statistical tests are used in arriving at these decisions. There are five ingredients to any statistical test : (a) Null Hypothesis (b) Alternate Hypothesis (c) Test Statistic (d) Rejection/Critical Region (e) Conclusion In attempting to reach a decision, it is useful to make an educated guess or assumption about the population involved, such as the type of distribution.
 * Statistic Name: Hypothesis Testing **
 * Alternative Names: confirmatory data analysis **
 * Central Debate: **
 * Simple Description ** :

The researcher states a hypothesis to be tested, formulates an analysis plan, analyzes sample data according to the plan, and accepts or rejects the null hypothesis, based on results of the analysis.  o Example A: what types of questions can this statistic/issue inform? o Example B:  o  Use concrete examples to maximize learning o Strengths o Weaknesses o Other options o Full description of each component in the formula o List of other formulas required to calculate the components. o Using unique data (either real or made-up) o Connect to research interests (if possible) o []
 * Why Essential: **
 * Critical Assumptions / Fundamental Challenges **
 * For this Test/Issue, discuss **
 * Formula(s) used to calculate the statistic **
 * Example **
 * External web links **