1. Generalizing a Sample’s Findings to Its Population and Testing Hypotheses About Percents and Means

2. Statistics Versus Parameters
Statistics: values that are computed from information provided by a sample
Parameters: values that are computed from a complete census which are considered to be precise and valid measures of the population
Parameters represent “what we wish to know” about a population. Statistics are used to estimate population parameters.
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4. The Concepts of Inference and Statistical Inference
Inference: drawing a conclusion based on some evidence
Statistical inference: a set of procedures in which the sample size and sample statistics are used to make estimates of population parameters
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6. How to Calculate Sample Error (Accuracy)
Where z = 1.96 (95%)
or 2.58 (99%) sp
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7. Accuracy Levels for Different Sample Sizes
The “p” you found in your sample
At 95% ( z = 1.96)
n p=50% p=70% p=90%
10 ±31.0% ±28.4% ±18.6%
100 ±9.8% ±9.0% ±5.9%
250 ±6.2% ±5.7% ±3.7%
500 ±4.4% ±4.0% ±2.6%
1,000 ±3.1% ±2.8% ±1.9%
1.96 times sp
95% Confidence interval: p ± 1.96 times sp
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8. Parameter Estimation
Parameter estimation: the process of using sample information to compute an interval that describes the range of values of a parameter such as the population mean or population percentage is likely to take on
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9. Parameter Estimation Parameter estimation involves three values:
Sample statistic (mean or percentage generated from sample data)
Standard error (variance divided by sample size; formula for standard error of the mean and another formula for standard error of the percentage)
Confidence interval (gives us a range within which a sample statistic will fall if we were to repeat the study many times over
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10. Parameter Estimation
Statistics are generated from sample data and are used to estimate population parameters.
The sample statistic may be either a percentage, i.e., 12% of the respondents stated they were “very likely” to patronize a new, upscale restaurant OR
The sample statistic may be a mean, i.e., the average amount spent per month in restaurants is $185.00
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11. Parameter Estimation
Standard error: while there are two formulas, one for a percentage and the other for a mean, both formulas have a measure of variability divided by sample size. Given the sample size, the more variability, the greater the standard error.
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12. Parameter Estimation
The lower the standard error, the more precisely our sample statistic will represent the population parameter. Researchers have an opportunity for predetermining standard error when they calculate the sample size required to accurately estimate a parameter. Recall Chapter 13 on sample size.
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13. Standard Error of the Mean
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14. Standard Error of the Percentage
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15. Parameter Estimation
Confidence intervals: the degree of accuracy desired by the researcher and stipulated as a level of confidence in the form of a percentage
Most commonly used level of confidence: 95%; corresponding to 1.96 standard errors
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16. Parameter Estimation
What does this mean? It means that we can say that if we did our study over 100 times, we can determine a range within which the sample statistic will fall 95 times out of 100 (95% level of confidence). This gives us confidence that the real population value falls within this range.
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17. How do I interpret the confidence interval?
Theoretical notion
Take many, many, many samples
Plot the p’s
95 % will fall in confidence interval
(p ± z times sp)
2.5%
2.5%
95%
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18. Parameter Estimation
Five steps involved in computing confidence intervals for a mean or percentage:
Determine the sample statistic
Determine the variability in the sample for that statistic
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19. Parameter Estimation Identify the sample size
Decide on the level of confidence
Perform the computations to determine the upper and lower boundaries of the confidence interval range
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20. Parameter Estimation Using SPSS: Estimating a Percentage
Run FREQUENCIES (on RADPROG) and you find that 41.3% listen to “Rock” music.
So, set p=41.3 and then q=58.7, n=400, 95%=1.96, calculate Sp.
The answer is 36.5%-46.1%
We are 95% confident that the true % of the population that listens to “Rock” falls between 36.5% and 46.1%. (See p. 464).
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21. How to Compute a Confidence Interval for a Percent
Determine the confidence interval using
Sample size (n)
95% level of confidence (z=1.96)
P=?%; q=100%-?%
Lower boundary Upper boundary
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22. Estimating a Population Percentage with SPSS
How do we interpret the results?
Our best estimate of the population percentage that prefers “Rock” radio is 41.3 percent, and we are 95 percent confident that the true population value is between 36.5 and 46.1 percent.
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23. Parameter Estimation Using SPSS: Estimating a Mean
SPSS will calculate a confidence interval around a mean sample statistic.
From the Hobbit’s Choice data assume
We want to know how much those who stated “very likely” to patronize an upscale restaurant spend in restaurants per month. (See p. 465.)
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24. Parameter Estimation Using SPSS: Estimating a Mean
We must first use DATA, SELECT CASES to select LIKELY=5.
Then we run ANALYZE, COMPARE MEANS, ONE SAMPLE T-TEST.
Note: You should only run this test when you have interval or ratio data.
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27. Parameter Estimation Using SPSS: Estimating a Percentage
Estimating a Percentage: SPSS will not calculate for a percentage. You must run FREQUENCIES to get your sample statistic and n size. Then use the formula p±1.96 Sp.
AN EXAMPLE: We want to estimate the percentage of the population that listens to “Rock” radio.
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28. Estimating a Population Percentage with SPSS
Suppose we wish to know how accurately the sample statistic estimates the percent listening to “Rock” music.
Our “best estimate” of the population percentage is 41.3% prefer “Rock” music stations (n=400). We run FREQUENCIES to learn this.
But how accurate is this estimate of the true population percentage preferring rock stations?
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29. Estimating a Population Mean with SPSS
How do we interpret the results?
My best estimate is that those “very likely” to patronize an upscale restaurant in the future, presently spend $281 dollars per month in a restaurant. In addition, I am 95% confident that the true population value falls between $267 and $297 (95% confidence interval). Therefore, Jeff Dean can be 95% confident that the second criterion for the forecasting model “passes” the test.
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30. Hypothesis Testing
Hypothesis: an expectation of what the population parameter value is
Hypothesis testing: a statistical procedure used to “accept” or “reject” the hypothesis based on sample information
Intuitive hypothesis testing: when someone uses something he or she has observed to see if it agrees with or refutes his or her belief about that topic
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31. Hypothesis Testing Statistical hypothesis testing:
Begin with a statement about what you believe exists in the population
Draw a random sample and determine the sample statistic
Compare the statistic to the hypothesized parameter
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32. Hypothesis Testing Statistical hypothesis testing:
Decide whether the sample supports the original hypothesis
If the sample does not support the hypothesis, revise the hypothesis to be consistent with the sample’s statistic
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33. What is a Statistical Hypothesis?
A hypothesis is what someone expects (or hypothesizes) the population percent or the average to be.
If your hypothesis is correct, it will fall in the confidence interval (known as supported).
If your hypothesis is incorrect, it will fall outside the confidence interval (known as not supported)
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34. How a Hypothesis Test Works
Test hypothesis
Sample Population
Exact amount---- Uses sample error
percent Test against Ho
average Test against Ho
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35. How to Test Statistical Hypothesis
2.5%
2.5%
95%
+1.96
-1.96
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36. Testing a Hypothesis of a Mean
Example in Text: Rex Reigen hypothesizes that college interns make $2,800 in commissions. A survey shows $2,750. Does the survey sample statistic support or fail to support Rex’s hypothesis? (p. 472)
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37. Since 1. 43 z falls between -1. 96z and +1
Since 1.43 z falls between -1.96z and z, we ACCEPT the hypothesis.
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38. How to Test Statistical Hypothesis
2.5%
95%
+1.96
-1.96
Supported
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Not Supported
Not Supported

39. The probability that our sample mean of $2,800 came from a distribution of means around a population parameter of $2,750 is 95%. Therefore, we accept Rex’s hypothesis.
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40. Hypothesis Testing
Non-Directional hypotheses: hypotheses that do not indicate the direction (greater than or less than) of a hypothesized value
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41. Hypothesis Testing
Directional hypotheses: hypotheses that indicate the direction in which you believe the population parameter falls relative to some target mean or percentage
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42. Using SPSS to Test Hypotheses About a Percentage
SPSS cannot test hypotheses about percentages; you must use the formula. See p. 475
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43. Using SPSS to Test Hypotheses About a Mean
In the Hobbit’s Choice Case we want to test that those stating “very likely” to patronize an upscale restaurant are willing to pay an average of $18 per entrée.
DATA, SELECT CASES, Likely=5
ANALYZE, COMAPRE MEANS, ONE SAMPLE T TEST
ENTER 18 AS TEST VALUE
Note: z value is reported as t in output.
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46. What if We Used a Directional Hypothesis?
Those stating “very likely” to patronize an upscale restaurant are willing to pay more than an average of $18 per entrée.
Is the sign (- or +) in the hypothesized direction? For “more than” hypotheses it should be +; if not, reject.
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47. What if We Used a Directional Hypothesis?
Since we are working with a direction, we are only concerned with one side of the normal distribution. Therefore, we need to adjust the critical values. We would accept this hypothesis if the z value computed is greater than (95%).
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