The table of descriptive statistics provides the baseline information.
Summary Statistics for the Data
N M SD Skew Kurt
Level1 4.000 2.000 2.449 0.544 -2.944
Level2 4.000 6.000 2.449 0.544 -2.944
The values of the group statistics are calculated separately for each level or condition. They are not identical to the values obtained from analyzing the variable as a whole.
The tables of inferential statistics show the key elements to be interpreted.
Confidence Interval for the Mean Difference
Diff SE df LL UL
Comparison 4.000 1.732 6.000 -0.238 8.238
Hypothesis Test for the Mean Difference
Diff SE df t p
Comparison 4.000 1.732 6.000 2.309 0.060
Confidence Interval for the Standardized Mean Difference
d SE LL UL
Comparison 1.633 0.943 -0.215 3.481
With t = 2.309, p = .060. This would not be considered a statistically significant finding.
Using the “Confidence Interval for the Mean Difference”, the researcher concludes that the true population mean difference is somewhere between -.238 and 8.238 (knowing that the estimate could be wrong).
Here, “Cohen’s d” equals 1.633. Given Cohen’s heuristics for interpreting effect sizes, this would be considered an extremely large effect.
For this analysis, the emphasis is on comparing the means from two groups. Here again the summary and the inferential statistics focus on the difference.
An independent samples t test showed that the difference in Outcome scores between the first group (n = 4, M = 4.00, SD = 2.45) and the second group (n = 3, M = 6.00, SD = 2.45) was very large but not statistically significant, t(6) = 2.31, p = .060, 95% CI [-0.24, 8.24], d = 1.63.
Alternatively, the means, standard deviations, and confidence intervals could be presented in a table or figure associated with this text.