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MINDLESS SIGNIFICANCE TESTING
Decision science news has a post on hypothesis testing that I find relevant.
Some well-made points grow old while no one pays attention to them. One of the most embarrassing for social science is its categorical perception of p-values.
Tender of kindred Web site Andrew Gelman and Hal Stern have an article whose name says it all: The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant.
Link to The Difference Between Significant and Not Significant is Not Statistically Significant
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AMA citation:
Quesada J. The Difference Between Significant and Not Significant is Not Statistically Significant. Academic Productivity. 2006. Available at: https://academicproductivity.com/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/. Accessed January 18, 2012.
APA citation:
Quesada, Jose. (2006). The Difference Between Significant and Not Significant is Not Statistically Significant. Retrieved January 18, 2012, from Academic Productivity Web site: https://academicproductivity.com/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/
Chicago citation:
Quesada, Jose. 2006. The Difference Between Significant and Not Significant is Not Statistically Significant. Academic Productivity. https://academicproductivity.com/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/ (accessed January 18, 2012).
Harvard citation:
Quesada, J 2006, The Difference Between Significant and Not Significant is Not Statistically Significant, Academic Productivity. Retrieved January 18, 2012, from
MLA citation:
Quesada, Jose. "The Difference Between Significant and Not Significant is Not Statistically Significant." 11 Dec. 2006. Academic Productivity. Accessed 18 Jan. 2012.
This entry was posted on Monday, December 11th, 2006 at 9:16 am and is filed under Blog, Statistics, Teaching. You can follow any responses to this entry through the feed. You can leave a response, or trackback from your own site.
May 28th, 2007 at 3:37 pm
[...] A fuller description of the possible alternatives can be found here. One of the advantages of the t-test is that it can be applied to a relatively small number of cases. It was specifically designed to evaluate statistical differences for samples of 30 or less. However, although the assumption of normality is not too important with large samples, it is important with small sample sizes, for example less than 10. You also need to be aware that statistically significant does not mean the same as practically significant. [...]
July 19th, 2007 at 7:52 am
[...] A fuller description of the possible alternatives can be found here. One of the advantages of the t-test is that it can be applied to a relatively small number of cases. It was specifically designed to evaluate statistical differences for samples of 30 or less. However, although the assumption of normality is not too important with large samples, it is important with small sample sizes, for example less than 10. You also need to be aware that statistically significant does not mean the same as practically significant. [...]
December 18th, 2007 at 5:54 pm
[...] 11, 2006 by WDW A post at Academic Productivity describes a new (to me) problem with statistical significance: the [...]
April 3rd, 2008 at 5:19 pm
[...] Be aware that the difference between significant and not significant is not statistically significant [...]