Descriptive and Inferential Statistics

Read these sections and complete the questions at the end of each section. Here, we introduce descriptive statistics using examples and discuss the difference between descriptive and inferential statistics. We also talk about samples and populations, explain how you can identify biased samples, and define differential statistics.

Inferential Statistics


  1.  Samples; populations
    We study a sample to allow us to draw inferences about the population.

  2. All of the above are false.
    Stratified sampling is more likely to be representative of the population than random sampling.

  3. random assignment has occurred.
    Random assignment has occurred because the decision as to which subject goes into which group is random.

  4. The only way to eliminate uncertainty is to obtain data from the whole population. You can reduce uncertainty with a large sample.

  5. All of the above except "biases your results". Random sampling does not produce bias, which means systematic rather than random error.

  6. A random sample is defined as one in which every sample of a particular size has an equal probability of being selected.

  7. The correct choice is: Lisa Meyer, Todd Jones, and Maria Rivera, whose ID numbers were picked from a table of random numbers.

  8. will likely have groups from the population over-represented or under-represented due to systematic sampling factors.
    Only when the sampling is systematically favoring one group or another is the sample biased. Random samples, although they can be different from the population, are not biased. Bias is defined by the procedure for drawing the sample, not by the result.