- Parameter - a parameter is a number that describes the population. A parameter is a fixed number, but in practice we don't know its value.
- Statistic - a statistic is a number that describes a sample. The value of a statistic is known when we have taken a sample, but it can change from sample to sample. We often use a statistic to estimate an unknown parameter.
- Bias - Bias is consistent, repeated deviation of the sample statistic from the population parameter in the same direction when we take many samples.
- Variability - variability describes how spread out the values of the sample statistic are when we take many samples. Large variability means that the result of sampling is not repeatable
- Note: a good sampling method has both small bias and small variability

**To reduce bias**, use random sampling. When we start with a list of the entire population, simple random sampling produces unbiased estimates - the values of a statistic computed from an SRS neither consistently overestimate nor consistently underestimate the value of the population parameter.**To reduce variability**of an SRS, use a larger sample. You can make the variability as small as you want by taking a large enough sample.

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