Page 51 - Statistics II for Dummies

Chapter 2: Finding the Right Analysis for the Job 35

Here are some tips for analyzing data and interpreting the results, in terms of
the statistical procedures and techniques that you may use — at school, in
your job, and in everyday life. These tips are implemented and reinforced
throughout this book:

✓ Be sure that the research question being asked is clear and definitive.
Some researchers don’t want to be pinned down on any particular set
of questions because they have the intent of mining the data — looking
for any relationship they can find and then stating their results after the
fact. This practice can lead to overanalyzing the data, making the results
subject to skepticism by statisticians.
✓ Double-check that you clearly understand the type of data being
collected. Is the data categorical or quantitative? The type of data used
drives the approach that you take in the analysis.
✓ Make sure that the statistical technique you use is designed to answer
the research question. If you want to make comparisons between two
groups and your data is quantitative, use a hypothesis test for two
means. If you want to compare five groups, use analysis of variance
(ANOVA). Use this book as a resource to help you determine the
technique you need.
✓ Look for the limitations of the data analysis. For example, if the
researcher wants to know whether negative political ads affect the
population of voters and she bases her study on a group of college
students, you can find severe limitations here. For starters, student
reactions to negative ads don’t necessarily carry over to all voters in
the population. In this case, it’s best to limit the conclusions to college
students in that class (which no researcher would ever want to do).
Better to take a sample that represents the intended population of all
voters in the first place (a much more difficult task, but well worth it).

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