Often inferential statistics help to draw conclusions about an entire population by looking at only a sample of the population. To infer is to conclude or judge from premises or evidence (American Heritage Dictionary) and not to prove. Note that the inferential statistics usually suggest but cannot absolutely prove an explanation or cause-and-effect relationship. The differences in attendance and drinks served between her parties and other parties would have to be large enough to draw any conclusions. Finding that less well-attended parties had on average fewer drinks served would suggest that your friend Sophia's drinks might be the important factor. Your next questions may be: Why are her parties so successful? Is it the food she serves, the size of her social network, the prestige of her job, the number of men or women she knows, her physical attractiveness, the alcohol she provides, or the location and size of her residence? Inferential statistics may help you answer these questions. Say comparative statistics suggest that parties hosted by your friend Sophia are very successful (e.g., the average number of attendees and the median duration of her parties are greater than those of other parties). It allows you to draw conclusions based on extrapolations, and is in that way fundamentally different from descriptive statistics that merely summarize the data that has actually been measured. Inferential statistics helps to suggest explanations for a situation or phenomenon. Lee, in Principles and Practice of Clinical Trial Medicine, 2008 I nferential S tatistics This assumption of independence can create a number of challenges for animal behavior researchers. That is each value in a condition is thought to be unrelated to any other value in the sample. Each replication in a condition is assumed to be independent. Virtually all inferential statistics have an important underlying assumption. Thus, the larger the sample of subjects, the more powerful the statistic is said to be. The larger the sample size, the more likely a statistic is to indicate that differences exist between the treatment groups. That value along with the degrees of freedom, a measure related to the sample size, and the rejection criteria are used to determine whether differences exist between the treatment groups. However, most inferential statistics are based on the principle that a test-statistic value is calculated on the basis of a particular formula. Researchers should consult the numerous texts on experimental design and statistics to find the right statistical test for their experiment. There are many types of inferential statistics and each is appropriate for a specific research design and sample characteristics. Inferential statistics use measurements from the sample of subjects in the experiment to compare the treatment groups and make generalizations about the larger population of subjects. Inferential statistics are often used to compare the differences between the treatment groups. Kuhar, in Encyclopedia of Animal Behavior, 2010 Inferential Statistics
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