A t test on these two groups, shifters vs. nonshifters, gave a `` t '' value of 2.405 which is significant on the two-tail test at the

Significance testing and confidence intervals for the mean can then be estimated with the t test.

* Martial art: The ability to use t ' ai chi ch ' uan as a form of self-defense in combat is the test of a student's understanding of the art.

* Configure the test environment ( ideally identical hardware to the production platform ), router configuration, quiet network ( we don ’ t want results upset by other users ), deployment of server instrumentation, database test sets developed, etc.

* Welch's t test, a statistical test intended for use with two samples having possibly unequal variances.

* The Prescient Are Few-...“ the number of funds that have beaten the market over their entire histories is so small that the False Discovery Rate test can ’ t eliminate the possibility that the few that did were merely false positives ” — just lucky, in other words.

She said the presence was attempting to interfere with the test: " He wants to take the card ; he doesn ’ t want me to read.

where F is the gravitational force, m is the mass of the test particle, R is the position of the test particle, is a unit vector in the direction of R, t is time, G is the gravitational constant, and ∇ is the del operator

; Efficiency: When normality holds, MWW has an ( asymptotic ) efficiency of or about 0. 95 when compared to the t test.

Overall, the robustness makes the MWW more widely applicable than the t test, and for large samples from the normal distribution, the efficiency loss compared to the t test is only 5 %, so one can recommend MWW as the default test for comparing interval or ordinal measurements with similar distributions.

MWW will give very similar results to performing an ordinary parametric two-sample t test on the rankings of the data.

In that situation, the unequal variances version of the t test is likely to give more reliable results, but only if normality holds.

Alternatively, some authors ( e. g. Conover ) suggest transforming the data to ranks ( if they are not already ranks ) and then performing the t test on the transformed data, the version of the t test used depending on whether or not the population variances are suspected to be different.