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There are strengths and weaknesses associated with statistical testing. For this discussion, begin by reviewing the various methods of statistical testing presented in your textbook (i.e., t-tests, ANOVA, chi-square, and f-tests). Then, keeping these methods in mind, read the following quotes:

• “Trying to analyze a situation without enough data was like looking at a photograph of a ball in flight and trying to gauge its direction. Is it going up, down, sideways? Is it about to collide with a baseball bat? Is it moving at all, or is something on the blind side holding it in place? A single frame didn’t mean a thing. Patterns were based on data. With enough datapoints, you could predict just about anything.”—Marcus Sakey, Brilliance (2013)


• “In a world with amazing amounts of statistics and demographics available, If you don’t utilize foresight, statistics, demographics, projections and predictions the competition will.”—Akutra-Ramses Atenosis Cea


• “The government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But you must never forget that every one of these figures comes in the first instance from the village watchman, who just puts down what he damn pleases.”—Josiah Stamp


• “No data are excluded on subjective or arbitrary grounds. No one piece of data is more highly valued than another. The consequences of this policy have to be accepted, even if they prove awkward.”—Jennifer K. McArthur, Place-Names in the Knossos Tablets Identification and Location (1993)


• “Statistical analysis in cases involving small numbers can be particularly helpful because on many occasions intuition can be highly misleading.”—Sandy Zabell, Statistics: A Guide to the Unknown (3rd ed.) (1989)

https://www.goodreads.com/quotes/search?utf8=%E2%9C%93&q=statistics&commit=Search (Links to an external site.)

Based on the above quotes, along with this week’s assigned readings and Instructor Guidance, discuss why statistical testing is important in research.