Inferential Statistics

# Inferential Statistics - • It must be large and varied...

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Inferential Statistics After analyzing statistics, researchers make inferences about how reliable and significant their data are. Example: The researcher’s survey of the students in three classes showed differences in how long the students studied for each course. The mean number of hours for students in Course A was about eight hours, and for students in Courses B and C, the average was about six hours. Does this mean Course A requires the most hours of study? Were the differences the researcher observed in study time real or just due to chance? In other words, can he generalize from the samples of students he surveyed to the whole population of students? He needs to determine the reliability and significance of his statistics. If researchers want to generalize confidently from a sample, the sample must fulfill two criteria:
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Unformatted text preview: • It must be large and varied enough to be representative. • It must not have much variation in scores. Researchers can use inferential statistics to figure out the likelihood that an observed difference was just due to chance. If it’s unlikely that the difference was due to chance, then the observed difference could be considered statistically significant. Psychologists usually consider a result to be statistically significant if such a result occurs just by chance 5 or fewer times out of every 100 times a study is done. They call this statistical significance at the p ≤ .05 level (p less than or equal to point oh-five). However, statistical significance alone does not make a finding important. Statistical significance simply means that a result is probably not due to chance....
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