Back to the Table of Contents

An Introduction to Statistics

Review of Statistics Lessons 1 and 2

Terms & Vocab., Lesson 1: Data & Measure.

  1. Descriptive Statistics characterizes or describes a data set.
  2. Inferential Statistics tries to infer information about a population from a sample.
  3. Statistics is a collection of methods used in planning an experiment and analyzing data.
  4. Population is the complete set of data elements.
  5. A sample is a selected portion of a population.
  6. Parameters characterize a population, whereas a statistic is a sample measure.
  7. Accuracy is a measure of rightness, whereas precision is a measure of exactness.
  8. Statistics can be misused is a variety of ways to prove most anyone's point of view.
  9. Data are plural, whereas datum is singular.
  10. Qualitative data are nonnumeric: good, better, best.
  11. Quantitative data are numeric and can be either discrete (quantized) or continuous.
  12. Being able to do simple math: fractions, percentages, etc., is important.
  13. There are four levels of measurement: nominal, ordinal, interval, ratio.
  14. Ratio is the highest level, data are interval and has a starting point (zero, like Kelvin).
  15. Interval data have meaningful intervals between measurements (Celsius, Fahrenheit).
  16. Ordinal data have order but lack meaningful intervals: (strongly) agree, disagree, etc.
  17. Nominal data have names only: brown, plaid, paisley.

Terms & Vocabulary, Lesson 2: Sampling

  1. Sample size is very important.
  2. Measure, don't ask.
  3. Random errors are ok, systematic errors need to be accounted for, sampling errors should be designed out. No randomization, no generalization.
  4. Sampling medium used (mail, phone, e-mail, personal interview) will affect accuracy.
  5. Sample must be representative of the population (avoid bias).
  6. Observational studies are more passive whereas experiments deliberately impose treatments on individuals. Can't always experiment. Experiments allow conclusions!
  7. There are five primary sampling methods.
  8. In random sampling any population member has a equal chance of being measured.
  9. In systematic sampling every kth member of the population is sampled.
  10. In stratified sampling the population is divided into two or more strata and each subpopulation is sampled.
  11. In cluster sampling a population is divided into clusters and a few of these clusters are exhaustively sampled.
  12. In convenience sampling the element can often select whether or not it is sampled.
  13. Be very wary of convenience sampling since it is prone to bias.
  14. Questions may be open ended (essay) or closed (multiple-choice, true/false).
  15. Studies may be retrospective (looking back) or prospective (looking ahead).