| d.single.t.t | R Documentation |
This function displays d and non-central confidence interval for single t estimated from the t-statistic.
d.single.t.t(t, n, a = 0.05)
t |
t-test value |
n |
sample size |
a |
significance level |
To calculate d, the t-statistic is divided by the square root of the sample size.
d = t / sqrt(n)
Learn more on our example page.
The effect size (Cohen's d) with associated confidence intervals and relevant statistics.
d |
effect size |
dlow |
lower level confidence interval d value |
dhigh |
upper level confidence interval d value |
n |
sample size |
df |
degrees of freedom (sample size - 1) |
t |
sig stats |
p |
p-value |
estimate |
the d statistic and confidence interval in APA style for markdown printing |
statistic |
the t-statistic in APA style for markdown printing |
#A school has a gifted/honors program that they claim is
#significantly better than others in the country. The gifted/honors
#students in this school scored an average of 1370 on the SAT,
#with a standard deviation of 112.7, while the national average
#for gifted programs is a SAT score of 1080.
gift = t.test(singt_data, mu = 1080, alternative = "two.sided")
#According to a single-sample t-test, the scores of the students
#from the program were significantly higher than the national
#average, t(14) = 9.97, p < .001.
#You can type in the numbers directly as shown below, or refer
#to your dataset within the function.
d.single.t.t(t = 9.968, n = 15, a = .05)
d.single.t.t(9.968, 15, .05)
d.single.t.t(gift$statistic, length(singt_data$SATscore), .05)