

   SSttuuddeenntt''ss tt--TTeesstt

        t.test(x, y = NULL, alternative = "two.sided", mu = 0, paired = FALSE,
               var.equal = FALSE, conf.level = 0.95)

   AArrgguummeennttss::

          x: a numeric vector of data values.

          y: an optional numeric vector data values.

   alternative: must be one of `"two.sided"', `"greater"' or
             `"less"'.  You can specify just the initial let-
             ter.  This parameter indicates the alternative
             hypothesis.

         mu: a number indicating the true value of the mean (or
             difference in means if you are performing a two
             sample test).

     paired: a logical indicating whether you want a paired t-
             test.

   var.equal: a logical variable indicating whether to treat
             the two variances as being equal. If `TRUE' then
             the pooled variance is used to estimate the vari-
             ance otherwise the Welch approximation to the
             degrees of freedom is used.

   conf.level: confidence level of the interval.

   DDeessccrriippttiioonn::

        t.test performs one and two sample t-tests on vectors
        of data.

   DDeettaaiillss::

        If `paired' is `TRUE' then both `x' and `y' must be
        specified and they must be the same length.  Missing
        values are removed (in pairs if `paired' is `TRUE').
        If `var.equal' is `TRUE' then the pooled estimate of
        the variance is used. If `var.equal' is `FALSE' then
        the variance is estimated separately for both groups
        and the Welch modification to the degrees of freedom is
        used.

   VVaalluuee::

        A list with class `"htest"' containing the following
        components:

   statistic: the value of the t-statistic.

   parameters: the degrees of freedom for the t-statistic.

    p.value: the p-value for the test.

   conf.int: a confidence interval for the mean appropriate to
             the specified alternative hypothesis.

   estimate: the estimated mean or difference in means depend-
             ing on whether it was a one-sample test or a two-
             sample test.

   null.value: the specified hypothesized value of the mean or
             mean difference depending on whether it was a one-
             sample test or a two-sample test.

   alternative: a character string describing the alternative
             hypothesis.

     method: a character string indicating what type of t-test
             was performed.

   data.name: a character string giving the name(s) of the
             data.

   EExxaammpplleess::

        t.test(1:10,y=c(7:20))      # P = .00001855
        t.test(1:10,y=c(7:20, 200)) # P = .1245    -- NOT significant anymore

