Hypothesis Testing •The intent of hypothesis testing is formally examine two opposing conjectures (hypotheses), H 0 and H A •These two hypotheses are mutually exclusive and exhaustive so that one is true to the exclusion of the other •We accumulate evidence - collect and analyze sample information - for the purpose of determining which of

Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true.

Definition of Statistical hypothesis They are hypothesis that are stated in such a way that they may be evaluated by appropriate statistical techniques. There are two hypotheses involved in hypothesis testing Null hypothesis H 0: It is the hypothesis to be tested . Alternative hypothesis H A: It is a statement of

HYPOTHESIS TESTING STEPS IN HYPOTHESIS TESTING Step 1: State the Hypotheses Null Hypothesis (H 0) in the general population there is no change, no difference, or no relationship; the independent variable will have no effect on the dependent variable o Example •All dogs have four legs. •There is no difference in the number of legs dogs have.

23.1 How Hypothesis Tests Are Reported in the News 1. Determine the null hypothesis and the alternative hypothesis. 2. Collect and summarize the data into a test statistic. 3. Use the test statistic to determine the p-value. 4. The result is statistically significant if the p-value is less than or equal to the level of significance.

Hypothesis Testing • Is also called significance testing • Tests a claim about a parameter using evidence (data in a sample • The technique is introduced by considering a one-sample z test • The procedure is broken into four steps • Each element of the procedure must be understood

The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief, or hypothesis, about a parameter. Is there statistical evidence, from a random sample of potential customers, to support the hypothesis that more than 10% of the potential customers will pur-chase a new product?

The major purpose of hypothesis testing is to choose between two competing hypotheses about the value of a population parameter. For example, one hypothesis might claim that the wages of men and women are equal, while the alternative might claim that men make more than women.

In this lecture note, we discuss the fundamentals of statistical hypothesis tests. Any statistical hypothesis test, no matter how complex it is, is based on the following logic of stochastic proof by contradiction.

ANOVA is an omnibus test. It compares all levels of all factors. When ANOVA is significant, it means “At least one of the means is different.” Which one(s)? By how much? Note: you lose power when conducting lots of tests – so be judicious and plan comparisons via hypotheses!