T Tests

Paired sample t-tests also revealed significant increases in social functioning (n = 67, p = .041), body image (nn = 71, p = .029), and treatment interventions (nn = 69, p = .002). ❋ Unknown (2010)

Analysis of individual domains was performed using paired-samples t-tests. ❋ Unknown (2010)

Also, t-tests should only be used when the scale of measurement employed in assigning 'numbers' is interval or ratio scale data; yours appears to be ordinal. ❋ Mark Reckons (2009)

In the social domain, the magnitude of the t-tests on these three variables range from .02 to 1.67; in the economic domain the magnitudes range from 7.13 to 15.19. ❋ Unknown (2009)

Also, t-tests should only be used when the scale of measurement employed in assigning 'numbers' is interval or ratio scale data; yours appears to be ordinal.um.. the t-test was applied to the majority numbers themselves, not the rankings derived from them. ❋ Mark Reckons (2009)

I wish I could publish research that comes with a ready made participant pool and only required basic t-tests. ❋ Field Notes (2008)

That's because, without going into details, multiple t-tests increase the chances of getting a statistically significant result just by coincidence. ❋ AYDIN (2007)

The fatal flaw is that Renker compared the means of the 20 samples using multiple t-tests. ❋ AYDIN (2007)

Then they used simple t-tests which assume independent values to compare the differences and to calculate error bounds. ❋ Unknown (2007)

Issue (1) essentially has to do with t-tests (or whatever the goshverdarnta relevant metrics are), inclusion of studies, quality of studies, etc. ❋ Unknown (2005)

My I guess naive understanding of spurious regression was that it was similar to the fallacy of the multiple t-tests: run twenty t-tests on sets of random noise and you can expect to get one 5% significant result. ❋ Unknown (2005)

The t-tests are generally weaker on the high P/E side, but 1/20 the “95% level” is generally considered the basic threshold for significance. ❋ David Dreman (1998)

Jonas insisted that good research generates numbers and statistics, t-tests and chi-squares, distributions and product moments—objective results that can be disputed and defended. ❋ M.D. Lewis Mebl-Madrona (1997)

Student's t-tests were used for statistical analysis. ❋ Thanyaluck Phitak (2010)

The use of one-sided post-hoc t-tests was justified by the directed assumptions of the two models that we considered; accumulation vs. prediction. ❋ Guido Hesselmann Et Al. (2010)

Namely, t-tests demonstrated that the estimates in 'saccade' condition differed significantly from zero only for the smallest distance (t (6) = 3.0, p = 0.024). ❋ Anna Oleksiak Et Al. (2010)