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:
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:
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.
Logo and design: © 2012 rapport Development Team | License (AGPL3) | Fork on GitHub | Styled with skeleton