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For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.

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  2. 2. OBJECTIVES W H AT A R E I N F E R E N T I A L S TAT I S T I C S ? T H E L O G I C O F I N F E R E N T I A L S TAT I S T I C S β€’ S A M P L I N G E R R O R β€’ D I S T R I B U T I O N O F S A M P L E M E A N S β€’ S TA N D A R D E R R O R O F T H E M E A N β€’ C O N F I D E N C E I N T E R VA L S β€’ C O N F I D E N C E I N T E R VA L S A N D P R O B A B I L I T Y β€’ C O M PA R I N G M O R E T H A N O N E S A M P L E β€’ T H E S TA N D A R D E R R O R O F T H E D I F F E R E N C E B E T W E E N S A M P L E M E A N S
  3. 3. WHAT ARE INFERENTIAL STATISTICS? Descriptive statistics are but one type of statistic that research use to analyze their data . Many times they wish to make inferences about a population based on a data they have obtained from a sample. To do this, they use inferential statistics. INFERENTIAL STATISTICS are certain types of procedures that allow a researchers to make inferences about a population based on findings from a sample. Making inferences about the populations on the basis of random samples is what inferential statistics is all about.
  4. 4. Suppose a researcher administers a commercially available IQ test to sample of 65 students selected from a particular elementary school district and finds their average score is 85. What does this tell her about the IQ scores of the entire population of students in the district? Does the average IQ score of students in the district also equal 85? Or is this sample of students different, on the average, from other students in the district? If these students are different, how are they different? Are their IQ scores higher –or lower?
  5. 5. When a sample is representative, all the characteristics of the population are assumed to be present in the sample in the same degree. No sampling procedure, not even random sampling, guarantees a totally representative sample, but the chance of obtaining one is greater with random sampling than with any other method. And the more a sample represents a population, the more researchers are entitled to assume that what they find out about the sample will also be true of that population.
  6. 6. Suppose a researcher is interested in the difference between males and females with respect to interest in history. He hypothesizes that female students find history more interesting than do male students. To testthe hypothesis, he decides to perform the following study. He obtains one random sample of 30 male history students from the population of 500 male tenth-grade students taking history in a nearby school district and another random sample of 30 female history students from the female population of 550 female tenth-grade history students in the district.
  8. 8. Will the mean score of the male group on the attitude test differ from the mean score of the female group? Is it reasonable to assume that each sample will give a fairly accurate picture of its population? On the other hand, the students in each sample are only a small portion of their population, and only rarely is a sample absolutely identical to its parent population on a given characteristic. The data the researcher obtains from the two samples will depend on the individual students selected to be in each sample. So how can the researcher be sure that any particular sample he has selected is, indeed, a representative one?
  9. 9. The data the researcher obtains from the two samples will depend on the individuals selected to be in each sample Samples are not likely to identical to their parent populations. The difference between a sample and its population is referred to as sampling error. No two samples from the same population will be the same in all their characteristics. Two different samples from the same population will not be identical: They will be composed of diff. individuals, they will have different scores on a test(or other measure) and they will probably have different sample means
  10. 10. FIGURE 11.2
  11. 11. DISTRIBUTION OF SAMPLE MEANS Large collections of random samples do pattern themselves in such a way that is possible for researchers to predict accurately some characteristics of the population from which the sample was selected. Were we able to select an infinite number of random samples ( all of the same size ) from a population, calculate the mean of each, and then arrange these means into a frequency polygon, we would find that they shaped themselves into a familiar pattern. The means of a large number of random samples tend to be normally distributed, unless the size of each of the sample is small ( n<30). Once n=30, the distribution of sample means is very nearly normal, even if the population is not normally distributed.
  12. 12. Like all normal distributions, a distribution of sample means (called a sampling distribution) has its own mean and a standard deviation. The mean of a sampling distribution(the β€œmean of the means”) is equal to the mean of the population. In an infinite number of samples, the results will vary. Consider the number 1,2 and 3. The population mean is 2. Now take all of the possible types of samples of size two. How many would there be? Does the mean of this sampling distribution equal to the whole population?
  13. 13. FIGURE 11.3
  14. 14. STANDARD ERROR OF THE MEAN Is the standard deviation of a sampling distribution. As in all normal distributions, therefore the 68-99-99.7 rule holds: approximately 68% of the sample means fall Β±1 SEM, approximately 95% percent fall between Β±2 SEM and 99.7% fall between Β±3 SEM. If we know or can accurately estimate the mean and the standard deviation of the sampling distribution, we can determine whether it is likely or unlikely that a particular sample mean could be obtained from that population.
  15. 15. FIGURE 11.4
  16. 16. It is possible to use z scores to describe the position of any particular sample mean within a distribution of sample means. Z scores is the simplest form of standard score. A z score simply states how far a score(or mean) differs from the mean of scores(or means) in standard deviation units. One z score = 1 standard deviation. The z score tells a researcher exactly where a particular sample is located related to all other sample means that could have obtained.
  18. 18. A LITTLE REVIEW 1. The sampling distribution of the mean ( or any descriptive statistics) is the distribution of the means ( or other statistic) obtained (theoretically) from an infinitely large number of samples of the same size. 2. The shape of the sampling distribution in many (but not all) cases is the shape of the normal distribution. 3. The SEM ( Standard Error of the Mean)- that is, the standard deviation of a sampling distribution of means--- can be estimated by dividing the standard deviation of the sample by the square root of the sample size minus one. 4. The frequency with which a particular sample mean will occur an be estimated by using z scores based on sample data to indicate its position in the sampling distribution
  19. 19. CONFIDENCE INTERVALS We can use the SEM to indicate boundaries or limits, within which the population mean lies. Such boundaries are called confidence intervals. How are they determined? Let us return to the example of the researcher who administered and IQ test. You will recall that she obtained a sample mean of 85 and wanted to know how much the population mean might differ from this value. We are now in a position to give her some help in this regard. Let us assume that we have calculated the estimated standard error of the mean for her sample and found it to equal to 2.0
  20. 20. Suppose this researcher then wished to established an interval that would give her more confidence than p=.95. in making a statement about the population mean. This can be done by calculating the 99 percent confidence
  21. 21. Our researcher can now answer her question about approximately how much the population mean differs from the sample mean. While she cannot know exactly the population mean is, she can indicate the β€˜boundaries’ or limits within which it is likely to fall. To repeat, these limits are called confidence intervals. The 95 percent confidence interval spans a segment on the horizontal axis that we are 95 percent certain contains the population mean. The 99 percent confidence interval spans a segment on the horizontal axis within which we are even more certain ( 99 percent certain) that the population mean falls.
  22. 22. CONFIDENCE INTERVALS AND PROBABILITY Probability is nothing more than predicted relative occurrence, or relative frequency. 5 in 100 is an example of probability The probability of the population mean being outside the 81.08- 88.92 limits (95 percent confidence interval) is only 5 in 100 The probability of the population mean being outside the 79.84- 90.16 limits (99 percent confidence interval) is even less--- 1 in 100
  23. 23. COMPARING MORE THAN ONE SAMPLE For example, a researcher might want to determine if there is a difference in attitude between 4th grade boys and girls in mathematics; whether there is a difference in achievement between students taught by the discussion method as compared to the lecture method; and so forth For example, if a difference between means is found between the test scores of two samples in a study, a researcher wants to know if a difference exists in the populations from which the two samples were selected.
  24. 24. DOES A SAMPLE DIFFERENCE REFLECT A POPULATION DIFFERENCE? Is the difference we have found a likely or an unlikely occurrence? POPULATION MEAN ??? POPULATION MEAN ??? SAMPLE A Mean = 25 SAMPLE B Mean = 22
  25. 25. THE STANDARD ERROR OF THE DIFFERENCE BETWEEN SAMPLE MEANS Differences between sample means are also likely to be normally distributed. The distribution of differences between sample means also has its own mean and standard deviation. The mean of the sampling distribution of differences between sample means of the two populations. The standard deviation of this distribution is called the standard error of the difference (SED) 𝑆𝐸𝐷 = (𝑆𝐸𝑀1)2 + (𝑆𝐸𝑀2)2