Description

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

Introduction

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:

Overview

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.

Descriptives

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

Table continues 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

Diagnostics

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.

Normality Tests

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..

Results

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

Description

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

Introduction

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:

Overview

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.

Descriptives

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

Table continues 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

Diagnostics

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.

Normality Tests

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..

Results

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.