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In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the (null) hypothesis that two populations have equal means. It is named for its creator, Bernard Lewis Welch , and is an adaptation of Student's t -test , [ 1 ] and is more reliable when the two samples have unequal variances and ...
This page was last edited on 17 December 2023, at 04:19 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
BTW, it makes sense to apply Welch's test routinely. If sample sizes are equal and variances are identical the result matches the conventional t-test. Therefore, in R by default Welch’s test is applied: t.test(data1, data2, alternative="two.sided") is equivalent to t.test(data1, data2, alternative="two.sided", var.equal=FALSE) One has to state
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From the t-test, the difference between the group means is 6-2=4. From the regression, the slope is also 4 indicating that a 1-unit change in drug dose (from 0 to 1) gives a 4-unit change in mean word recall (from 2 to 6). The t-test p-value for the difference in means, and the regression p-value for the slope, are both 0.00805. The methods ...
Display a year or month calendar Template parameters [Edit template data] Parameter Description Type Status Year year the ordinal year number of the calendar Default current Number suggested Month month whether to display a single month instead of a whole year, and which one Default empty Example current, next, last, 1, January String suggested Show year show_year whether to display the year ...
Print/export Download as PDF; ... Welch's t-test: interval: normal: unpaired: 2: No [8] ... This page was last edited on 5 July 2024, ...
A follow-up paper showed that the classic paired t-test is a central Behrens–Fisher problem with a non-zero population correlation coefficient and derived its corresponding probability density function by solving its associated non-central Behrens–Fisher problem with a nonzero population correlation coefficient. [14]