http://www.academicproductivity.com/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/ A survival guide for the 21st century researcher Sun, 14 Mar 2010 19:14:35 +0000 http://wordpress.org/?v=2.9.2 hourly 1 http://www.academicproductivity.com/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/comment-page-1/#comment-22715 行中水 » Blog Archive » ZZ:How to complete your PhD Thu, 03 Apr 2008 17:19:09 +0000 http://www.academicproductivity.com/blog/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/#comment-22715 [...] Be aware that the difference between significant and not significant is not statistically significant [...] [...] Be aware that the difference between significant and not significant is not statistically significant [...]

]]> http://www.academicproductivity.com/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/comment-page-1/#comment-14194 Misuses of “Significant” (#5) « Policy Economist Tue, 18 Dec 2007 17:54:17 +0000 http://www.academicproductivity.com/blog/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/#comment-14194 [...] 11, 2006 by WDW A post at Academic Productivity describes a new (to me) problem with statistical significance: the [...] [...] 11, 2006 by WDW A post at Academic Productivity describes a new (to me) problem with statistical significance: the [...]

]]> http://www.academicproductivity.com/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/comment-page-1/#comment-1831 learnsigma Thu, 19 Jul 2007 07:52:02 +0000 http://www.academicproductivity.com/blog/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/#comment-1831 [...] 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. [...] [...] 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. [...]

]]> http://www.academicproductivity.com/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/comment-page-1/#comment-1101 One sample t-test « learnsigma Mon, 28 May 2007 15:37:49 +0000 http://www.academicproductivity.com/blog/2006/the-difference-between-significant-and-not-significant-is-not-statistically-significant/#comment-1101 [...] 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. [...] [...] 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. [...]

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