Comprehensive step-by-step guide for estimating descriptive group metrics, executing multiple pairwise contrast tests, generating compact letter displays (CLD), and exporting multi-domain reports.
The Mean Comparison & Grouping Module in DATES provides a unified framework for computing group-level descriptive statistics and evaluating statistical significance between group means. Whether conducting physical laboratory trials, clinical cohort studies, material testing, environmental monitoring, or social science surveys, this module enables researchers across all scientific fields to assess differences between treatment levels.
Key Analytical Capabilities:
The sidebar configuration panel and top header controls let you customize data mapping, post-hoc statistical procedures, significance thresholds, and output formatting:
| Parameter / Setting | Description | Statistical Purpose | Recommended Setting |
|---|---|---|---|
| Factor Column | Selects the categorical grouping column (e.g., Treatment_Group, Temperature_Level, Dosage_Tier). | Defines the discrete treatment groups for mean calculation and pairwise comparisons. | Required. Select the primary experimental classification column. |
| Replication Column | Optional column containing replicate identifier tags (e.g., Rep_1, Rep_2, Block_A). | Tracks individual experimental unit replicates per treatment group. | Optional. Map if replicate tracking is present in raw data. |
| Target Response Variables | Selects continuous quantitative measurement columns to evaluate. | Computes mean tables, standard errors, pairwise contrasts, and plots for selected variables. | Select one or multiple numeric response trait columns. |
| Alpha Level | Sets significance error threshold (5% / 0.05 or 1% / 0.01). | Establishes the critical confidence limit for adjusted p-values and lettering groupings. | Use 5% (0.05) for standard research; use 1% (0.01) for strict control. |
| Decimal Precision | Controls rounding for display tables and charts (1, 2, 3, or 4 places). | Ensures uniform formatting across summary tables and export documents. | Set to 2 decimal places for general reporting or 3-4 for fine precision. |
| Mean Separation Test | Selects post-hoc comparison method (None, Tukey, Dunnett, LSD, Duncan, Holm, Bonferroni, Šidák, Scheffé, Games-Howell). | Adjusts p-values and pairwise confidence bounds for multiple testing bias. | Use Tukey for all-pairwise comparisons; use Dunnett for control vs treatment comparisons; use Games-Howell under unequal variances. |
| Control Level | Selects reference control group level when Dunnett post-hoc method is active. | Compares every experimental treatment group specifically against the designated baseline control group. | Appears automatically when Dunnett is selected. Pick the baseline/control level. |
| Mean Ordering | Sorts group mean summary rows: High → Low (Descending) or Low → High (Ascending). |
Organizes treatment ranking tables to highlight highest or lowest performing groups. | Select High → Low when searching for top performing factor levels. |
The dataset spreadsheet (.xlsx or .csv) must follow a tidy tabular structure. Each row represents a single observation or experimental trial unit, containing categorical factor columns and quantitative response measurement columns:
| Replicate | Treatment_Group | Temperature_Level | Response_Metric_1 | Response_Metric_2 |
|---|---|---|---|---|
| Rep_1 | Control_Baseline | Ambient_20C | 102.45 | 14.20 |
| Rep_2 | Control_Baseline | Ambient_20C | 104.10 | 14.85 |
| Rep_3 | Control_Baseline | Ambient_20C | 101.80 | 13.90 |
| Rep_1 | Condition_Alpha | Elevated_35C | 128.60 | 19.40 |
| Rep_2 | Condition_Alpha | Elevated_35C | 131.20 | 20.10 |
| Rep_3 | Condition_Alpha | Elevated_35C | 126.90 | 18.95 |
| Rep_1 | Condition_Beta | High_50C | 115.30 | 16.70 |
| Rep_2 | Condition_Beta | High_50C | 117.80 | 17.30 |
| Rep_3 | Condition_Beta | High_50C | 114.20 | 16.15 |
All statistical computations in this module are defined in plain text concepts below:
Plain Text Definition:
The arithmetic average of observed values for a given treatment factor level, calculated as the total sum of valid observations in that group divided by the group sample size count (N).
Plain Text Definition:
Measures the dispersion or spread of individual observations around their group mean. Calculated as the square root of the average squared deviations from the group mean, divided by sample size minus one.
Plain Text Definition:
Quantifies the precision of the estimated group mean. Calculated by dividing the group sample standard deviation by the square root of the group sample size count (N).
Plain Text Definition:
The net numerical difference between the estimated mean of Group A and the estimated mean of Group B for a selected response variable.
Plain Text Definition:
The calculated probability of observing a pairwise mean difference as large as the observed difference by random chance, adjusted according to the selected post-hoc procedure (such as Tukey or Bonferroni) to control family-wise error rate.
Plain Text Definition:
An automated lettering code assigned to group means. Treatment groups that share at least one common letter (for example, 'a' and 'ab') are not statistically significantly different at the chosen alpha significance level.
Below is an example of a Group Mean Summary Table with Tukey HSD Compact Letter Displays:
| Treatment Level | Sample Count (N) | Mean Value | Standard Error (SE) | Standard Deviation (SD) | Letter Grouping |
|---|---|---|---|---|---|
| Condition_Alpha | 12 | 128.90 | 1.15 | 3.98 | a |
| Condition_Beta | 12 | 115.77 | 1.08 | 3.74 | b |
| Condition_Gamma | 12 | 112.40 | 1.22 | 4.23 | b |
| Control_Baseline | 12 | 102.78 | 0.98 | 3.40 | c |
Condition_Alpha (Mean = 128.90, Letter = 'a') is significantly higher than all other conditions.Condition_Beta (115.77, 'b') and Condition_Gamma (112.40, 'b') share the letter 'b', indicating no statistically significant difference between these two treatments at the 5% alpha level.Control_Baseline (102.78, 'c') has the lowest mean and differs significantly from all active treatment conditions.Use Tukey HSD when comparing all treatment pairs against each other with equal group sizes. Use Dunnett when testing treatments solely against a designated baseline control. Use Games-Howell when group variances are unequal (heteroscedasticity).
Ensure quantitative response columns contain clean numerical data without non-numeric text strings. The module automatically filters out missing blank cells and reports exact N counts per group.
If you use the DATES Mean Comparison module for experimental data analysis in published scientific research, please cite it as follows: