A t-test report with table of descriptives, diagnostic tests and t-test specific statistics.

In a nutshell, *t-test* is a statistical test that assesses hypothesis of equality of two means. But in theory, any hypothesis test that yields statistic which follows *t-distribution* can be considered a *t-test*. The most common usage of *t-test* is to:

- compare the mean of a variable with given test mean value -
**one-sample***t-test* - compare means of two variables from independent samples -
**independent samples***t-test* - compare means of two variables from dependent samples -
**paired-samples***t-test*

Independent samples *t-test* is carried out with *Internet usage in leisure time (hours per day)* as dependent variable, and *Gender* as independent variable. Confidence interval is set to 95%. Equality of variances wasn't assumed.

In order to get more insight on the underlying data, a table of basic descriptive statistics is displayed below.

Gender | min | max | mean | sd | var | median | IQR |
---|---|---|---|---|---|---|---|

male | 0 | 12 | 3.27 | 1.953 | 3.816 | 3 | 3 |

female | 0 | 12 | 3.064 | 2.355 | 5.544 | 2 | 3 |

skewness | kurtosis |
---|---|

0.9443 | 0.9858 |

1.398 | 1.87 |

Since *t-test* is a parametric technique, it sets some basic assumptions on distribution shape: it has to be *normal* (or approximately normal). A few normality test are to be applied, in order to screen possible departures from normality.

We will use *Shapiro-Wilk*, *Lilliefors* and *Anderson-Darling* tests to screen departures from normality in the response variable (*Internet usage in leisure time (hours per day)*).

N | p | |
---|---|---|

Shapiro-Wilk normality test | 0.9001 | 1.618e-20 |

Lilliefors (Kolmogorov-Smirnov) normality test | 0.168 | 3e-52 |

Anderson-Darling normality test | 18.75 | 7.261e-44 |

As you can see, applied tests yield different results on hypotheses of normality, so you may want to stick with one you find most appropriate or you trust the most..

Welch Two Sample t-test was applied, and significant differences were found.

statistic | df | p | CI(lower) | CI(upper) | |
---|---|---|---|---|---|

t |
1.148 | 457.9 | 0.2514 | -0.1463 | 0.5576 |

A t-test report with table of descriptives, diagnostic tests and t-test specific statistics.

In a nutshell, *t-test* is a statistical test that assesses hypothesis of equality of two means. But in theory, any hypothesis test that yields statistic which follows *t-distribution* can be considered a *t-test*. The most common usage of *t-test* is to:

- compare the mean of a variable with given test mean value -
**one-sample***t-test* - compare means of two variables from independent samples -
**independent samples***t-test* - compare means of two variables from dependent samples -
**paired-samples***t-test*

One-sample *t-test* is carried out with *Internet usage in leisure time (hours per day)* as dependent variable. Confidence interval is set to 95%. Equality of variances wasn't assumed.

In order to get more insight on the underlying data, a table of basic descriptive statistics is displayed below.

Variable | min | max | mean | sd | var |
---|---|---|---|---|---|

Internet usage in leisure time (hours per day) | 0 | 12 | 3.199 | 2.144 | 4.595 |

median | IQR | skewness | kurtosis |
---|---|---|---|

3 | 2 | 1.185 | 1.533 |

Since *t-test* is a parametric technique, it sets some basic assumptions on distribution shape: it has to be *normal* (or approximately normal). A few normality test are to be applied, in order to screen possible departures from normality.

We will use *Shapiro-Wilk*, *Lilliefors* and *Anderson-Darling* tests to screen departures from normality in the response variable (*Internet usage in leisure time (hours per day)*).

N | p | |
---|---|---|

Shapiro-Wilk normality test | 0.9001 | 1.618e-20 |

Lilliefors (Kolmogorov-Smirnov) normality test | 0.168 | 3e-52 |

Anderson-Darling normality test | 18.75 | 7.261e-44 |

As you can see, applied tests yield different results on hypotheses of normality, so you may want to stick with one you find most appropriate or you trust the most..

One Sample t-test was applied, and significant differences were found.

statistic | df | p | CI(lower) | CI(upper) | |
---|---|---|---|---|---|

t |
-0.007198 | 671 | 0.9943 | 3.037 | 3.362 |

This report was generated with R (3.0.1) and rapport (0.51) in *0.88* sec on x86_64-unknown-linux-gnu platform.

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