# d.single.t.t: d for Single t from t In MOTE: Effect Size and Confidence Interval Calculator

## Description

This function displays d and non-central confidence interval for single t estimated from the t-statistic.

## Usage

 `1` ```d.single.t.t(t, n, a = 0.05) ```

## Arguments

 `t` t-test value `n` sample size `a` significance level

## Details

To calculate d, the t-statistic is divided by the square root of the sample size.

d = t / sqrt(n)

## Value

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```#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) ```

MOTE documentation built on May 2, 2019, 5:51 a.m.