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A. Hypothesis Testing ● PPT Content ○ Developing Null and Alternative Hypothesis ■ Hypothesis Testing - can be used to determine whether a statement about the value of a population parameter should or should not be rejected. ● The null hypothesis, denoted by H 0 , is a tentative assumption about a p...

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A. Hypothesis Testing ● PPT Content ○ Developing Null and Alternative Hypothesis ■ Hypothesis Testing - can be used to determine whether a statement about population parameter should or should not be rejected. ● The null hypothesis, denoted by H0, is a tentative assumption about parameter. ●

The alternative hypothesis, denoted by Ha, is the opposite of what i hypothesis.

● The hypothesis testing procedure uses data from a sample to test the statements indicated by H0 and Ha. ● Developing Null and Alternative Hypotheses ○ Not always obvious how null and alternative hypotheses should ○ Care must be taken to structure the hypotheses appropriately conclusion provides the information the researcher wants ○ Context of situation is very important in determining how the be stated ○ In some cases, it is easier to identify the alternative hypothesis the null is easier ○ Correct hypothesis formulation will take practice ● Alternative hypothesis as a Research Hypothesis ○ Many applications of hypothesis testing involve an attempt to g support of a research hypothesis ○ In such cases, it is often best to begin with the alternative hypo the conclusion that the researcher hopes to support. ○ The conclusion that the research hypothesis is true is made if provide sufficient evidence to show that the null hypothesis ca ○ E.g. Example: A new teaching method is developed that is believed to be bett method. ○ Alternative Hypothesis: ■ The new teaching method is better. ■ The new bonus plan increases sales. ■ The new drug lowers blood pressure more than the exis ○ Null Hypothesis: ■ The new method is no better than the old method. ■ The new bonus plan does not increase sales. ■ The new drug does not lower blood pressure more than Null Hypothesis as an Assumption to be Challenged •We might begin with a belief or assumption that a statement about the value of a population parameter is true. •We then using a hypothesis test to challenge the assumption and determine if there is statistical

evidence to conclude that the assumption is Incorrect. •In these situations, it is helpful to develop the null

● The equality part of the hypotheses always appears in the null hypothesis. ● In general, a hypothesis test about the value of a population mean m must take on three forms (where m0 is the hypothesized value of the population mean).

Null and Alternative Hypotheses ● Example: Metro EMS ○ A major west coast city provides one of the most comprehensive emergency med world. Operating in a multiple hospital system with approximately 20 mobile med goal is to respond to medical emergencies with a mean time of 12 minutes or less. ● The director of medical services wants to formulate a hypothesis test that could u emergency response times to determine whether or not the service goal of 12 min achieved. The emergency service is meeting the response goal; no follow-up action is necessary. The emergency service is not meeting the response goal; appropriate follow-up action is Necessary. where: m = mean response time for the population of medical emergency requests ● [Video Application 2] ● Null hypothesis - An assumption we test, we do everyday ○ Assumed, Status quo, given - “This is accepted as true, let’s tes ○ H0 - Null means nothing new or different; assumption or statu ○ Assumed to be “true”; a given. ○ Negation of the research question ○ Always contains an equality (=, Less than or equal to, greater th ● Alternative - when we fail to accept the null hypothesis, we reject the ○ Unknown, Assertion, Claim - “This might be true, let’s test it. If something else.” ○ Ha - alternative is simply the other option when the null is reje ○ Rejection of an assumption or the given ○ Research question to be “proven” ○ Does not contain equality (not equal to, less than, greater than ● All statistical conclusions are made in reference to the null hypothesi

● As researchers, we either reject the null hypothesis or fail to reject th do not accept the null. ● If we reject the null hypothesis then we conclude the data supports t

○ a = 0.05 ○ a = 0.01 ○ One-Tailed and Two-Tailed Test ■ p-Value Approach to One-Tailed Hypothesis Testing ● The p-value is the probability, computed using the test statistic, that support (or lack of support) provided by the sample for the null hypo If the p-value is less than or equal to the level of significance , the va statistic is in the rejection region. ● One-Tailed Tests About a Population Mean:

●

s Known Example: Metro EMS The response times for a random sample of 40 medical emergencies were tabulate is 13.25 minutes. The population standard deviation is believed to be 3.2 minutes. The EMS director wants to perform a hypothesis test, with a .05 level of significan whether the service goal of 12 minutes or less is being achieved.

Steps of Hypothesis Testing Step 1. Develop the null and alternative hypotheses. Step 2. Specify the level of significance . Step 3. Collect the sample data and compute the value of the test statistic. p-Value Approach Step 4. Use the value of the test statistic to compute the p-value. Step 5. Reject H0 if p-value < a. Critical Value Approach Step 4. Use the level of significanceto determine the critical value and the rejectio Step 5. Use the value of the test statistic and the rejection rule to determine whether to reject H0. ○ Type I and Type II Errors ■ Type I Error ● Because hypothesis tests are based on sample data, we must allow fo errors. ● A Type I error is rejecting H0 when it is true. ● The probability of making a Type I error when the null hypothesis is t called the level of significance. ● Applications of hypothesis testing that only control the Type I error a significance tests. ■ Type II Error ● Type II Error ● A Type II error is accepting H0 when it is false. ● It is difficult to control for the probability of making a Type II error ● Statisticians avoid the risk of making a Type II error by using “do no “accept H0”.

■ [Video Application 1] ○ Inference about Difference about Two Population Means

B. Sampling Methods ● PPT Content ○ Population and sample...