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**Regarding a Parameter – Single Mean & Single Proportion**

Hypothesis Tests Regarding a Parameter – Single Mean & Single Proportion

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Overview This is the other part of inferential statistics, hypothesis testing Hypothesis testing and estimation are two different approaches to two similar problems Estimation is the process of using sample data to estimate the value of a population parameter Hypothesis testing is the process of using sample data to test a claim about the value of a population parameter

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**The Language of Hypothesis Testing**

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**Determine the null and alternative hypotheses from a claim**

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Hypothesis Testing The environment of our problem is that we want to test whether a particular claim is believable, or not The process that we use is called hypothesis testing This is one of the most common goals of statistics

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**Hypothesis Testing Hypothesis testing involves two steps**

Step 1 – to state what we think is true Step 2 – to quantify how confident we are in our claim The first step is relatively easy The second step is why we need statistics

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Hypothesis Testing We are usually told what the claim is, what the goal of the test is Now similar to estimation in the previous unit discussed, we will again use the material regarding the sampling distribution of the sample mean to quantify how confident we are in our claim

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**Example An example of what we want to quantify**

A car manufacturer claims that a certain model of car achieves 29 miles per gallon To test for the claim, we then test some number of cars We calculate the sample mean … it is 27 Is 27 miles per gallon consistent with the manufacturer’s claim? How confident are we that the manufacturer has significantly overstated the miles per gallon achievable?

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Example How confident are we that the gas economy is definitely less than 29 miles per gallon? We would like to make either a statement “We’re pretty sure that the mileage is less than 29 mpg” or “It’s believable that the mileage is equal to 29 mpg”

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Level of Significance A hypothesis test for an unknown parameter is a test of a specific claim Compare this to a confidence interval which gives an interval of numbers, not a “believe it” or “don’t believe it” answer The level of significance reflects the confidence we have in our conclusion

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**Null Hypothesis How do we state our claim? Our claim**

Is the statement to be tested Is called the null hypothesis Is written as H0 (and is read as “H-naught”)

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**Alternative Hypothesis**

How do we state our counter-claim? Our counter-claim Is the opposite of the statement to be tested Is called the alternative hypothesis Is written as H1 (and is read as “H-one”)

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Two-tailed Test There are different types of null hypothesis / alternative hypothesis pairs, depending on the claim and the counter-claim One type of H0 / H1 pair, called a two-tailed (or two-sided) test, tests whether the parameter is either equal to, versus not equal to, some value H0: parameter = some value H1: parameter ≠ some value

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**Example An example of a two-tailed test**

A bolt manufacturer claims that the diameter of the bolts average 10 mm H0: Diameter = 10 H1: Diameter ≠ 10 An alternative hypothesis of “≠ 10” is appropriate since A sample diameter that is too high may be a problem A sample diameter that is too low may also be a problem That is, we may reject the claim under the H0 , if the sample value is either too high or too low Thus this is a two-tailed test

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Left-tailed Test Another type of pair, called a left-tailed test, tests whether the parameter is either equal to, versus less than, some value H0: parameter = some value (This actually means Parameter some value) H1: parameter < some value Note: Equality sign appears only in the null hypothesis.

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**Example An example of a left-tailed test**

A car manufacturer claims that the mpg of a certain model car is at least 29.0 H0: MPG = (In fact, this does not mean MPG is only it means MPG ) H1: MPG < 29.0 An alternative hypothesis of “< 29” is appropriate since A mpg that is too low is a problem A mpg that is too high is not a problem That is, we reject the claim under the H0, if the sample mpg observed is too low, much lower than 29. Thus this is a left-tailed test. (The side of the tail depends on the direction under H1 which tends to support a lower value of MPG. And a lower value is located on the left of a higher value on a number line. Note: By convention, we always only put the equality sign for the claim in H0 , even though it should be MPG This is because we can tell the actual direction of the inequality sign under H0 by just looking at the sign in H1 (H0 is the opposite of H1. Since MPG is less than 29 in H1, MPG will be no less than 29 under H0.)

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Right-tailed Test Another third type of pair, called a right-tailed test, tests whether the parameter is either equal to, versus greater than, some value H0: parameter = some value (This actually means parameter Some value.) H1: parameter > some value Note: Equality sign appears only under the null hypothesis H0

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**Example An example of a right-tailed test**

A bolt manufacturer claims that the defective rate of their product is at most 1 part in 1,000 H0: Defect Rate = 0.001 H1: Defect Rate > 0.001 An alternative hypothesis of “> 0.001” is appropriate since A defect rate that is too low is not a problem A defect rate that is too high is a problem That is, higher defective rate observed tends to be in favor of H1, , but against H0. Thus this is a right-tailed test

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**One-tailed and Two-tailed Tests**

A comparison of the three types of tests The null hypothesis We believe that this is true The alternative hypothesis Type of test Sample value that is too low Sample value that is too high Two-tailed test A problem Left-tailed test Not a problem Right-tailed test

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Example 1 A manufacturer claims that there are at least two scoops of cranberries in each box of cereal What would be a problem? The parameter to be tested is the number of scoops of cranberries in each box of cereal If the sample mean is too low, that is a problem If the sample mean is too high, that is not a problem This is a left-tailed test The “bad case” is when there are too few

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Example 2 A manufacturer claims that there are exactly 500 mg of a medication in each tablet What would be a problem? The parameter to be tested is the amount of a medication in each tablet If the sample mean is too low, that is a problem If the sample mean is too high, that is a problem too This is a two-tailed test A “bad case” is when there are too few

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Example 3 A manufacturer claims that there are at most 8 grams of fat per serving What would be a problem? The parameter to be tested is the number of grams of fat in each serving If the sample mean is too low, that is not a problem If the sample mean is too high, that is a problem This is a right-tailed test The “bad case” is when there are too many

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**Reject or Not to reject H0**

There are two possible results for a hypothesis test If we believe that the null hypothesis could be true, this is called not rejecting the null hypothesis Note that this is only “we believe … could be” If we are pretty sure that the null hypothesis is not true, so that the alternative hypothesis is true, this is called rejecting the null hypothesis Note that this is “we are pretty sure that … is”

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**Understand Type I and Type II errors**

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Decision Errors In comparing our conclusion (not reject or reject the null hypothesis) with reality, we could either be right or we could be wrong When we reject (and state that the null hypothesis is false) but the null hypothesis is actually true When we not reject (and state that the null hypothesis could be true) but the null hypothesis is actually false These would be undesirable errors

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**Type I and II Errors A summary of the errors is**

We see that there are four possibilities … in two of which we are correct and in two of which we are incorrect

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Type I and II Errors When we reject the null hypothesis (and state that the null hypothesis is false) but the null hypothesis is actually true … this is called a Type I error When we do not reject the null hypothesis (and state that the null hypothesis could be true) but the null hypothesis is actually false … this called a Type II error In general, Type I errors are considered the more serious of the two

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Example A very good analogy for Type I and Type II errors is in comparing it to a criminal trial In the US judicial system, the defendant “is innocent until proven guilty” Thus the defendant is presumed to be innocent The null hypothesis is that the defendant is innocent H0: the defendant is innocent

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**Example (continued) If the defendant is not innocent, then**

The defendant is guilty The alternative hypothesis is that the defendant is guilty H1: the defendant is guilty The summary of the set-up H0: the defendant is innocent

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**Example (continued) Our possible conclusions**

Reject the null hypothesis Go with the alternative hypothesis H1: the defendant is guilty We vote “guilty” Do not reject the null hypothesis Go with the null hypothesis H0: the defendant is innocent We vote “not guilty” (which is not the same as voting innocent! Voting “not guilty” does not prove the defendant is innocent, we just do not have enough evidence to against the defendant.)

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**Example (continued) A Type I error A Type II error**

Reject the null hypothesis The null hypothesis was actually true We voted “guilty” for an innocent defendant A Type II error Do not reject the null hypothesis The alternative hypothesis was actually true We voted “not guilty” for a guilty defendant

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**Example (continued) Which error do we try to control?**

Type I error (sending an innocent person to jail) The evidence was “beyond a reasonable doubt” We must be pretty sure Very bad! We want to minimize this type of error A Type II error (letting a guilty person go) The evidence wasn’t “beyond a reasonable doubt” We weren’t sure enough If this happens … well … it’s not as bad as a Type I error (according to the US system)

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**State Conclusion to Hypothesis Tests**

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**Reject or Not to reject H0**

“Innocent” versus “Not Guilty” This is an important concept Innocent is not the same as not guilty Innocent – the person did not commit the crime Not guilty – there is not enough evidence to convict … that the reality is unclear To not reject the null hypothesis – doesn’t mean that the null hypothesis is true – just that there isn’t enough evidence to reject

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Summary A hypothesis test tests whether a claim is believable or not, compared to the alternative We test the null hypothesis H0 versus the alternative hypothesis H1 If there is sufficient evidence to conclude that H0 is false, we reject the null hypothesis If there is insufficient evidence to conclude that H0 is false, we do not reject the null hypothesis

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**Hypothesis Tests for a Population Mean **

Assuming the Population Standard Deviation is Known

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**Learning Objectives Understand the logic of hypothesis testing**

Test hypotheses about a population mean with σ known using the classical approach Test hypotheses about a population mean with σ known using P-values approach Test hypotheses about a population mean with σ known using confidence intervals approach

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**Understand the logic of hypothesis testing**

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Decision Rule Hypothesis test is to set up a decision rule for the sample data to reject or not to reject the null hypothesis How do we quantify “unlikely” the null hypothesis is true? What is the exact procedure to get to a “do not reject” or “reject” conclusion?

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**Methods of Hypothesis Testing**

There are three equivalent ways to perform a hypothesis test They will reach the same conclusion The methods The classical approach The P-value approach The confidence interval approach

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**Methods of Hypothesis Testing**

The classical approach If the sample value observed is too many standard deviations away from the true value claimed under H0, then it must be too unlikely H0 is true The P-value approach If the probability of the sample value being that far away is small, then it must be too unlikely H0 is true The confidence interval approach If we are not sufficiently confident that the parameter is likely enough, then it must be too unlikely Don’t worry … we’ll be explaining more

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**Basic Steps to Test the Hypothesis**

Step 1: We set up the null hypothesis that the actual mean μ is equal to a value μ0 and the alternative hypothesis Step 2: We set up a criterion (to reject H0) A criterion that quantifies “unlikely” the null hypothesis that the actual mean μ being equal to a specified value of μ0 is true. That is, the actual mean is unlikely to be equal to μ0

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**Collect Sample Data The three methods all need information**

We run an experiment We collect the data We calculate the sample mean The three methods all make the same assumptions to be able to make the statistical calculations That the sample is a simple random sample That the sample mean has a normal distribution

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**Choose a Test Statistic**

We first assume that the population standard deviation σ is known We use a sample estimate, for instance, a sample mean to test for the population parameter - the population mean μ We can apply our techniques if either The population has a normal distribution Our sample size n is large (n ≥ 30) In those cases, the distribution of the sample mean is normal with mean μ and standard deviation σ / √ n

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**Check the criterion for Unlikely**

The three methods all compare the observed results with the criterion that quantifies “unlikely”: Classical – how many standard deviations P-value – the size of the probability Confidence interval – inside or outside the interval If the results are unlikely based on these criterion, we reject the claim under the null hypothesis.

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**Statistical Significance**

The three methods all conclude similarly We do not reject the null hypothesis, or We reject the null hypothesis When we reject the null hypothesis, we say that the result is statistically significant

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**Perform Hypothesis Testing**

We now will cover how each of the Classical P-value, and Confidence interval approaches will show us how to conclude whether the result is statistically significant or not

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**Test hypotheses about a population mean with σ known using the classical approach**

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**The Classical Approach**

We compare the sample mean to the hypothesized population mean μ0 Measure the difference in units of standard deviations, which is called the test statistic: A lot of standard deviations is far … few standard deviations is not far Just like using a general normal distribution

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**α Level of Significance**

How far is too far? For example, we can set α = 0.05 as the size of “unlikely”, so-called “the level of significance” “Unlikely” means that this difference occurs with probability α = 0.05 of the time, or less under the null hypothesis This concept applies to two-tailed tests, left-tailed tests, and right-tailed tests Note: α is often determined subjectively before the experiment. It sets up a rule to reject the null hypothesis. So, it is also the size of the risk for committing a type I error of rejecting the null hypothesis by mistake.

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**5% Level of Significance**

For two-tailed tests The least likely 5% is the lowest 2.5% and highest 2.5% (below –1.96 and above standard deviations) … –1.96 and are the critical values (There are two critical values for 2-tailed test) The region outside this is the rejection region (or critical region) which covers the range of “ unlikely” values for the test statistic to reject H0

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**5% Level of Significance**

For left-tailed tests The least likely 5% is the lowest 5% (below –1.645 standard deviations) … –1.645 is the critical value (only one critical value for one-tailed test.) The region less than this is the rejection region

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**5% Level of Significance**

For right-tailed tests The least likely 5% is the highest 5% (above standard deviations) … is the critical value The region greater than this is the rejection region

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**Example 1 An example of a two-tailed test**

A bolt manufacturer claims that the diameter of the bolts average 10.0 mm H0: Diameter = 10.0 H1: Diameter ≠ 10.0 We take a sample of size 40 (Somehow) We know that the standard deviation of the population is 0.3 mm The sample mean is mm We’ll use a level of significance α = 0.05

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**Example 1 (continued) Do we reject the null hypothesis?**

10.12 is 0.12 higher than 10.0 The standard error is (0.3 / √ 40) = 0.047 The test statistic is 2.53 One of the two critical normal values is za/2, for α = 0.05, is 1.96 2.53 is more than 1.96, which is in the rejection region. Our conclusion We reject the null hypothesis We have sufficient evidence that the population mean diameter is not 10.0

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**Example 2 An example of a left-tailed test**

A car manufacturer claims that the mpg of a certain model car is at least 29.0 H0: MPG = 29.0 H1: MPG < 29.0 We take a sample of size 40 (Somehow) We know that the standard deviation of the population is 0.5 The sample mean mpg is 28.89 We’ll use a level of significance α = 0.05

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**Example 2 (continued) Do we reject the null hypothesis? Our conclusion**

28.89 is 0.11 lower than 29.0 The standard error is (0.5 / √ 40) = 0.079 The test statistic is -1.39 -1.39 is greater than , which is the left-tailed critical value -za, with α = is not in the rejection region. Our conclusion We do not reject the null hypothesis We have insufficient evidence that the population mean mpg is less than 29.0

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**Example 3 An example of a right-tailed test**

A bolt manufacturer claims that the defective rate of their product is at most 1.70 per 1,000 H0: Defect Rate = 1.70 H1: Defect Rate > 1.70 We take a sample of size 40 (Somehow) We know that the standard deviation of the population is .06 The sample defect rate is 1.78 We’ll use a level of significance α = 0.05

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**Example 3 (continued) Do we reject the null hypothesis? Our conclusion**

1.78 is 0.08 higher than 1.70 The standard error is (0.06 / √ 40) = 0.009 The test statistic is 8.43 8.43 is more than which is the right-tailed critical value za, with α = 0.05 Our conclusion We reject the null hypothesis We have sufficient evidence that the population mean rate is more than 1.70

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**Critical Value(s) and Rejection Region**

Two-tailed test The critical values are zα/2 and -zα/2 The rejection region includes {less than -zα/2} and {greater than z1-α/2} Left-tailed test The critical value is -zα The rejection region is {less than -zα} Right-tailed test The critical value is zα The rejection region is {greater than zα}

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Summary The general picture for a level of significance α

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**Decision Rule for Classical Approach**

Calculate a test For a significance level α provided, we locate the critical value(s) and corresponding rejection region. Reject the null hypothesis, if a calculated test statistic z0 is in the rejection region; Do not reject, otherwise.

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**Test hypotheses about a population mean with σ known using P-values**

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The P-value Approach The P-value is the probability of observing a sample mean that is as or more extreme than the observed The probability is calculated assuming that the null hypothesis is true We use the P-value to quantify how unlikely the sample mean is

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P-value Just like in the classical approach, we calculate the test statistic We then calculate the p-value, the probability that the sample mean would be this, or more extreme, if the null hypothesis was true. It measures how likely the observed z, i.e. z0 will occur under the null hypothesis. The two-tailed, left-tailed, and right-tailed calculations are slightly different

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P-value For the two-tailed test, the “unlikely” region are values that are too high and too low Small P-values corresponds to situations where it is unlikely to be this far away

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P-value For the left-tailed test, the “unlikely” region are values that are too low Small P-values corresponds to situations where it is unlikely to be this low

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P-value For the right-tailed test, the “unlikely” region are values that are too high Small P-values corresponds to situations where it is unlikely to be this high

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**Summary For all three models (two-tailed, left-tailed, right-tailed)**

The larger P-values mean that the difference is not relatively large … that it’s not an unlikely event The smaller P-values mean that the difference is relatively large … that it’s an unlikely event

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**Example Larger P-values**

A P-value of 0.30, for example, means that this value, or more extreme, could happen 30% of the time 30% of the time is not unusual Smaller P-values A P-value of 0.01, for example, means that this value, or more extreme, could happen only 1% of the time 1% of the time is unusual

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**Decision Rule for P-value Approach**

The decision rule is For a significance level α provided Do not reject the null hypothesis if the P-value is greater than α Reject the null hypothesis if the P-value is less than α For example, if α = 0.05 A P-value of 0.30 is likely enough, compared to a criterion of 0.05 level of significance A P-value of 0.01 is unlikely, compared to a criterion of 0.05 level of significance

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**Example 1 An example of a two-tailed test**

A bolt manufacturer claims that the diameter of the bolts average 10.0 mm H0: Diameter = 10.0 H1: Diameter ≠ 10.0 We take a sample of size 40 (Somehow) We know that the standard deviation of the population is 0.3 mm The sample mean is mm We’ll use a level of significance α = 0.05 Note: The claim is about the average is equal to 10.0 or not. It does not indicate it is going to be greater than or less than 10.0 if it is not equal to So, we design a two-tailed hypotheses, since we do not know the direction.

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**Example 1 (continued) Do we reject the null hypothesis?**

10.12 is 0.12 higher than 10.0 The standard error is ( ) = 0.047 The test statistic z0 is 2.53: ( )/0.047) = 2.53 The 2-sided P-value of 2.53 is < 0.05 = α P value = or 2 x normalcdf(2.53, E99) = or 2 x normalcdf(10.12, E99, 10, ) = Our conclusion We reject the null hypothesis We have sufficient evidence that the population mean diameter is not 10.0

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**Example 2 An example of a left-tailed test**

A car manufacturer claims that the mpg of a certain model car is at least 29.0 H0: MPG = 29.0 H1: MPG < 29.0 We take a sample of size 40 (Somehow) We know that the standard deviation of the population is 0.5 The sample mean mpg is 28.89 We’ll use a level of significance α = 0.05 Note: Since the equality sign should always appear only in the null hypothesis H0, , the claim of at least (greater than or equal to) 29.0 is placed under the null hypothesis. Since the alternative hypothesis H1 is the opposite of the null hypothesis H0, so MPG < 29 under H1.

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**Example 2 (continued) Do we reject the null hypothesis?**

28.89 is 0.11 lower than 29.0 The standard error is ( ) = 0.079 The test statistic is -1.39 The 1-sided P-value of is > 0.05 = α P value = or normalcdf(-E99, -1.39) = or normalcdf(-E99, 28.89, 29, ) = Our conclusion We do not reject the null hypothesis We have insufficient evidence that the population mean mpg is less than 29.0

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**Example 3 An example of a right-tailed test**

A bolt manufacturer claims that the defective rate of their product is at most 1.70 per 1,000 H0: Defect Rate = (This is the claim of at most 1.00 per 1,0000) H1: Defect Rate > 1.70 We take a sample of size 40 (Somehow) We know that the standard deviation of the population is .06 The sample defect rate is 1.78 We’ll use a level of significance α = 0.05

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**Example 3 (continued) Do we reject the null hypothesis?**

1.78 is 0.08 higher than 1.70 The standard error is ( ) = 0.009 The test statistic is 8.43 The 1-sided P-value of 8.43 is extremely small P-value = or normalcdf(1.74, E99, 1.70, ) = 1.75E-17 normalcdf(8.43, E99) = 1.75E-17 Our conclusion We reject the null hypothesis We have sufficient evidence that the population mean rate is more than 1.70

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**Classical and P-value Approaches**

Compare the rejection regions for the classical approach and the P-value approach They are the same Classical P-Value Note: The classical approach sets a criteria for “unlikely” in terms of a z value; the p-value approach sets a criteria in terms of a probability.

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**Test hypotheses about a population mean with σ known using confidence intervals**

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**Level of Significance α and Level of Confidence (1 – α)**

The confidence interval approach yields the same result as the classical approach and as the P-value approach We compare A hypothesis test with a level of significance α to A confidence interval with confidence (1 – α) •100% These are the same α’s

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**Decision Rule for Confidence Interval Approach**

The relationship is The hypothesis test calculation and the confidence interval calculation are very similar Not rejecting the hypothesis μ0 is inside the Confidence interval Rejecting the hypothesis μ0 is outside the Confidence interval

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**Example 1 An example of a two-tailed test**

A bolt manufacturer claims that the diameter of the bolts average 10.0 mm H0: Diameter = 10.0 H1: Diameter ≠ 10.0 We take a sample of size 40 (Somehow) We know that the standard deviation of this measurement is 0.3 mm The sample mean is mm We’ll use a level of significance α = 0.05

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**Example 1 (continued) Do we reject the null hypothesis? Our conclusion**

10.12 is 0.12 higher than 10.0 The standard error is (0.3 / √ 40) = 0.047 The confidence interval is ± 1.96 • 0.047, or to 10.21 10.0 is outside (10.03, 10.21) Our conclusion We reject the null hypothesis We have sufficient evidence that the population mean diameter is not 10.0 Note: 1.96 is z0.025 for a 95% confidence interval.

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**Example 2 An example of a left-tailed test**

A car manufacturer claims that the mpg of a certain model car is at least 29.0 H0: MPG = 29.0 H1: MPG < 29.0 We take a sample of size 40 (Somehow) We know that the standard deviation of the population is 0.5 The sample mean mpg is 28.89 We’ll use a level of significance α = 0.05

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**Example 2 (continued) Do we reject the null hypothesis? Our conclusion**

28.89 is 0.11 lower than 29.0 The standard error is (0.5 / √ 40) = 0.079 The upper confidence interval limit is • 0.079, or 29.02 29.0 is inside (-∞, 29.02) Our conclusion We do not reject the null hypothesis We have insufficient evidence that the population mean mpg is less than 29.0 Note: is z0.05 for a 95% upper confidence interval limit.

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**Example 3 An example of a right-tailed test**

A bolt manufacturer claims that the defective rate of their product is at most 1.70 per 1,000 H0: Defect Rate = 1.70 H1: Defect Rate > 1.70 We take a sample of size 40 (Somehow) We know that the standard deviation of the population is .06 The sample defect rate is 1.78 We’ll use a level of significance α = 0.05

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**Example 3 (continued) Do we reject the null hypothesis? Our conclusion**

1.78 is 0.08 higher than 1.70 The standard error is (0.06 / √ 40) = 0.009 The lower confidence interval limit is 1.78 – • = 1.76 1.70 is outside (1.76, ∞) Our conclusion We reject the null hypothesis We have sufficient evidence that the population mean rate is more than 1.70 Note: is z0.05 for a 95% lower confidence interval limit.

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Summary A hypothesis test of means compares whether the true mean is either Equal to, or not equal to, μ0 Equal to, or less than, μ0 Equal to, or more than, μ0 There are three equivalent methods of performing the hypothesis test The classical approach The P-value approach The confidence interval approach

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**Hypothesis Tests for a Population Mean in Practice**

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**Test hypotheses about a population mean with σ unknown**

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**Test of Mean in Practice**

So far, we assumed that the population standard deviation, σ, was known This is not a realistic assumption There is a parallel between last unit and this unit solving the problems assuming that σ was known solving the problem assuming that σ was not known σ not being known is a much more practical assumption

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**Test of Mean in Practice**

The parallel between Confidence Intervals and Hypothesis Tests carries over here too For Confidence Intervals We estimate the population standard deviation σ by the sample standard deviation s We use the Student’s t-distribution with n-1 degrees of freedom For Hypothesis Tests, we do the same Use s for σ Use the Student’s t for the normal

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**t-test Statistic Thus instead of the z-test statistic knowing σ**

we calculate a t-test statistic using s This is the appropriate test statistic to use when σ is unknown

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Hypotheses We can perform our hypotheses for tests of a population proportion in the same way as when the sample standard deviation is known Two-tailed Left-tailed Right-tailed H0: μ = μ0 H1: μ ≠ μ0 H1: μ < μ0 H1: μ > μ0

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**Test of Mean in Practice**

The process for a hypothesis test of a mean, when σ is unknown is not different from the test of a men, when σ is known Set up the problem with a null and alternative hypotheses Collect the data and compute the sample mean Compute the test statistic

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**Classical and P-value Approaches**

Either the Classical and the P-value approach can be applied to determine the significance P-value approach Classical approach

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**Test of Mean in Practice**

There are thus only differences between this process and the one using the normal distribution previously We use the sample standard deviation s instead of the population standard deviation σ We use the Student’s t-distribution, with n-1 degrees of freedom, instead of the normal distribution

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Example An example A gasoline manufacturer wants to make sure that the octane in their gasoline is at least 87.0 The testing organization takes a sample of size 40 The sample standard deviation is 0.5 ( i.e. s = 1.5) The sample mean octane is 86.94 Our null and alternative hypotheses H0: Mean octane = 87 HA: Mean octane < 87

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Example (continued) Classical Approach: Do we reject the null hypothesis under 0.05 level of significance? 86.94 is 0.06 lower than 87.0 The standard error is (0.5 / √ 40) = 0.08 0.06 is 0.75 standard error less The critical t value t0.05, with 39 degrees of freedom, is [obtained from TI calculator invT(.95,39)=-1.685] < , it is not unusual Our conclusion We do not reject the null hypothesis We have insufficient evidence that the true population mean (mean octane) is less than 87.0

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**Example (continued) P-value approach:**

Do we reject the null hypothesis under 0.05 level of significance? 86.94 is 0.06 lower than 87.0 The standard error is (0.5 / √ 40) = 0.08 0.06 is 0.75 standard error less The 1-sided P-value of is > 0.05 = α P value = = or tcdf(-E99, -0.75, 39) = Our conclusion We do not reject the null hypothesis We have insufficient evidence that the true population mean (mean octane) is less than 87.0

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**Compare t-test with z-test**

Comparing using the classical approach

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**Compare t-test with z-test**

Comparing using the P-value approach

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Summary A hypothesis test of means, with σ unknown, has the same general structure as a hypothesis test of means with σ known Any one of our three methods can be used, with the following two changes to all the calculations Use the sample standard deviation s in place of the population standard deviation σ Use the Student’s t-distribution in place of the normal distribution

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**Hypothesis Tests for a Population Proportion**

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**Test hypotheses about a population proportion using the normal model**

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**Test of Population Proportion**

In a sample of size n, with x successes, the best estimate of the population proportion is Similar to tests for means, we have Two-tailed tests Left-tailed tests Right-tailed tests

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**Standard Error of Sample Proportion**

Just as for confidence intervals, the standard error of the sample mean proportion is

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**Standard Error of the Sample Proportion under H0**

To test for the population proportion, we use the following standard error of the sample proportion: and not (Yes, use this) (No, don’t use this)

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**z-test Statistic for Testing the Proportion**

Because we assume that the null hypothesis H0: p = p0 is true, we should use as the standard error The test statistic is thus Z =

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**Hypothesis Test of Proportion**

We can perform our hypotheses for tests of a population proportion in the same way as the hypothesis tests of a population mean Two-tailed Left-tailed Right-tailed H0: p = p0 H1: p ≠ p0 H1: p < p0 H1: p > p0

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**Hypothesis Test of Proportion**

The process for a hypothesis test of a proportion is Set up the problem with a null and alternative hypotheses Collect the data and compute the sample proportion Compute the test statistic Z =

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**Classical and P-value Approaches**

Either the Classical and the P-value approach can be applied to determine the significance Classical approach P-value approach

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Example An example We believe that 60% of students prefer hamburgers over hot dogs A random sample of 200 students found that 102 of them preferred hamburgers. At α = 0.05, does the data support our belief? The sample size n = 200 The hypothesized proportion p0 = 0.60 The sample proportion

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**Example (continued) Our hypotheses The standard error is**

H0: p = 0.60 H1: p ≠ 0.60 The standard error is The test statistic is

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**Example (continued) The critical values for α = 0.05 are**

± z0.025 = ± 1.96 The test statistic –2.60 is outside the critical values, so we reject the null hypothesis There is significant evidence that the proportion of students who prefer hamburgers is not 60%

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Summary We can perform hypothesis tests of proportions in similar ways as hypothesis tests of means Two-tailed, left-tailed, and right-tailed tests The normal distribution should be used to compute the critical value(s) for this test

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**Putting It All Together: Which Method Do I Use?**

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**Determine the appropriate hypothesis test to perform**

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Which Test? Parallels between hypothesis tests and confidence intervals Both use the concept of the variability of a sample statistic Both use critical values from the normal and Student’s t-distributions Both have means with known σ, means with unknown σ, proportions, and standard deviations cases

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Which Test? It should not be surprising that the decision process for which hypothesis test to use is very similar to the decision process for which confidence interval to use Start with Is the parameter a mean? Is the parameter a proportion?

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**Which Test? In analyzing population means**

Is the population variance known? If so, then we can use the normal distribution If the population variance is not known If we have “enough” data (30 or more values), we still can use the normal distribution If we don’t have “enough” data (29 or fewer values), we should use the t-distribution We don’t have to ask this question in the analysis of proportions

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**The data is OK (reasonably normal)**

Which Test? For the test of a population mean If The data is OK (reasonably normal) The variance is known then we can use the normal distribution with a test statistic of Z=

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**Which Test? For the test of a population mean If**

The data is OK (reasonably normal) The variance is NOT known then we can use the Student’s t-distribution with a test statistic of t =

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**Which Test For the test of a population proportion**

If the sample size is large enough, we can use the proportions method with a test statistic of Z=

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**Summary The main questions that determine the method Is it a**

Population mean? Population proportion? In the case of a population mean, we need to determine Is the population variance known? Does the data look reasonably normal?

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Summary The process of hypothesis testing is very similar across the testing of different parameters The major steps in hypothesis testing are Formulate the appropriate null and alternative hypotheses Calculate the test statistic Determine the appropriate critical value or values Reach the reject / do not reject conclusions

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**Summary Similarities in hypothesis test processes Parameter**

Mean (Std Dev known) Mean (Std Dev unknown) Proportion H0: μ = μ0 p = p0 (2-tailed) H1: μ ≠ μ0 p ≠ p0 (L-tailed) H1: μ < μ0 p < p0 (R-tailed) H1: μ > μ0 p > p0 Test statistic Difference Critical value Normal Student t

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Summary We can test whether sample data supports a hypothesis claim about a population mean or a proportion We can use any one of three methods The classical method The P-Value method The Confidence Interval method The commonality between the three methods is that they set a criterion for rejecting or not rejecting the test statistic. The classical approach sets a criteria in terms of a z value; the p-value approach sets a criteria in terms of a probability.

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Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 20 Testing Hypotheses About Proportions.

Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 20 Testing Hypotheses About Proportions.

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