Description Usage Arguments Details Value Examples

This function displays d and non-central confidence interval for single t from means.

1 | ```
d.single.t(m, u, sd, n, a = 0.05)
``` |

`m` |
sample mean |

`u` |
population mean |

`sd` |
sample standard deviation |

`n` |
sample size |

`a` |
significance level |

To calculate d, the population is subtracted from the sample mean, which is then divided by the standard deviation.

d = (m - u) / sd

Learn more on our example page.

`d` |
effect size |

`dlow` |
lower level confidence interval d value |

`dhigh` |
upper level confidence interval d value |

`m` |
sample mean |

`sd` |
standard deviation of the sample |

`se` |
standard error of the sample |

`Mlow` |
lower level confidence interval of the sample mean |

`Mhigh` |
upper level confidence interval of the sample mean |

`u` |
population mean |

`n` |
sample size |

`df` |
degrees of freedom (n - 1) |

`t` |
t-statistic |

`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 |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
#The following example is derived from the "singt_data" dataset included
#in the MOTE library.
#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")
#You can type in the numbers directly as shown below,
#or refer to your dataset within the function.
d.single.t(m = 1370, u = 1080, sd = 112.7, n = 14, a = .05)
d.single.t(1370, 1080, 112.7, 100, .05)
d.single.t(gift$estimate, gift$null.value,
sd(singt_data$SATscore),
length(singt_data$SATscore), .05)
``` |

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