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9 Cards in this Set
- Front
- Back
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Inferential Statistics
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using the sample information to make inference about the population
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Sample Statistics
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calculated values from the sample (e.g. mean, median, mode, std. dev., range, etc.)
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Population parameter
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unknown value that is used to characterize a population
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Sampling distribution
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the distribution of all possible outcomes and their likelihood for some statistic
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Sampling distribution of sample mean
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sampling distribution of possible means that could occur all of same size (N)
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The mean of the sampling distribution of the sampling mean
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µX=µ. It is always close to the true population mean
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Standard error
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standard deviation of the SDSM; it estimates the variability in the possible sample means one could get
sigma xbar = sigma/sqr.rt.(n) |
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Central limit theorem
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theory that states that as the sample size increases, the sample distribution of the sample mean will look more like a normal distribution even if the population is not a normal distribution
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Error bars
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bars extended from the mean on a graph that is = to 1 standard error
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