The tables of descriptive statistics provide the baseline information.
> lapply(list(Outcome1, Outcome2), function(x) c(length(x), mean(x), sd(x)))
[[1]]
[1] 4.00000 2.00000 2.44949
[[2]]
[1] 4.00000 6.00000 2.44949
The descriptive statistics are calculated separately for each variable.
The table of inferential statistics show the key elements to be interpreted.
> cor.test(Outcome1, Outcome2)
Pearson's product-moment correlation
data: Outcome1 and Outcome2
t = 0.8165, df = 2, p-value = 0.5
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.8876337 0.9868586
sample estimates:
cor
0.5
The correlation calculation represents the relationship between the two variables. Here, r = .500 indicates a positive relationship.
In the Hypothesis Tests table, t provides the standardized statistic for testing whether the correlation differs from zero. This t value has a two-tailed probability (p) of .500, which would mean that this is not a statistically significant correlation.
The output also provides a confidence interval for the correlation.
Correlations provide a measure of statistical relationship between two variables. Note that correlations can also have inferential information associated with them (and that this information should be summarized if it is available and of interest).
For the participants (N = 4), the scores on Outcome 1 (M = 2.00, SD = 2.45) and Outcome 2 (M = 6.00, SD = 2.45) were moderately but not statistically significantly correlated, r(2) = .50, 95% CI [-0.89, 0.99], p = .500.