Chapter 5: Misconceptions

When people read, hear, or prepare research summaries, they sometimes have misconceptions about what is or isn't "sound practice" regarding the collection, analysis, and interpretation of data. Here are some of these common (and dangerous) misconceptions associated with the content of Chapter 5.

  1. Lengthy, detailed descriptions of samples are not only boring but actually bad because they detract from the most important part of the research report, the results.
  2. If data are collected from each and every person the researcher can possible use in his/her investigation, no inferential statistics should be used.
  3. A large sample that's not random is superior to a small sample that is random.
  4. In order to extract a stratified random sample from a population, the population must be subdivided so as to create strata that are the same size.
  5. If a researcher ends up with a response rate for a particular mailed survey or questionnaire that's higher than the "typical" response rate reported in authoritative texts written about research methodology, then he/she is entitled to feel pleased about his/her response rate.
  6. Statistical inference can only be used in conjunction with probability samples.
  7. You can determine whether or not a sample was selected randomly from a tangible population by looking to see if the sample is "odd" in some way.
  8. All samples are random samples.
  9. In studies having a less than desirable response rate to a mailed questionnaire or survey, the best way to check to see if there's a "response bias" is to compare "early returns" vs. "late returns."
  10. A study cannot yield interesting, valuable, and generalizable findings unless its findings are derived from random samples.

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Schuyler W. Huck
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