h2(#description). Description A t-test report with table of descriptives, diagnostic tests and t-test specific statistics. h3(#introduction). 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_":https://en.wikipedia.org/wiki/Student%27s_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_* h3(#overview). 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. h3(#descriptives). 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
h3(#diagnostics). 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. h4(#normality-tests). 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.. h3(#results). 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
h2(#description-1). Description A t-test report with table of descriptives, diagnostic tests and t-test specific statistics. h3(#introduction-1). 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_":https://en.wikipedia.org/wiki/Student%27s_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_* h3(#overview-1). 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. h3(#descriptives-1). 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
h3(#diagnostics-1). 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. h4(#normality-tests-1). 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.. h3(#results-1). 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
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