Mean Comparison & Grouping Module User Guide

1. OVERVIEW & OBJECTIVES

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:

Descriptive Group Metrics: Calculates group sample sizes (N), group means, standard deviations (SD), standard errors (SE), and missing value counts per treatment level.
Multiple Mean Separation Tests: Supports 10 distinct post-hoc testing procedures including Tukey HSD, Fisher LSD, Dunnett, Duncan DMRT, Holm, Bonferroni, Šidák, Scheffé, and Games-Howell.
Compact Letter Displays (CLD): Automatically assigns letter grouping codes (e.g., a, b, ab, c) to treatment means where treatments sharing common letters are not significantly different.
Flexible Mean Sorting: Sorts resulting treatment rank tables in descending (High → Low) or ascending (Low → High) order based on experimental goals.
Publication Visualizations & Reports: Renders interactive bar charts with error bars and letter labels, boxplots, pairwise contrast plots, automated AI textual summaries, and multi-format exports (Excel, Word, PowerPoint).

2. CONFIGURATION & PARAMETERS

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.

3. INPUT DATA FORMAT REQUIREMENTS

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

4. STATISTICAL FOUNDATIONS & METRICS (PLAIN TEXT DEFINITIONS)

All statistical computations in this module are defined in plain text concepts below:

Group Mean

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).

Standard Deviation (SD)

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.

Standard Error of Mean (SE)

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).

Pairwise Difference Estimate

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.

Adjusted p-Value

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.

Compact Letter Display (CLD)

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.

5. STEP-BY-STEP WORKFLOW

Upload Spreadsheet: Open the sidebar panel and drag-and-drop or click to upload your spreadsheet file (.xlsx or .csv).
Select Worksheet Tab: Choose the target worksheet if your workbook contains multiple tabs.
Map Categorical Factor: Select your treatment grouping column in the Factor dropdown menu.
Select Replicate Column (Optional): Select your replication tracking column if available.
Select Quantitative Variables: Check one or more continuous measurement columns from the available variable pills.
Set Alpha & Decimals: In the top header bar, select 5% or 1% significance alpha, and pick your preferred decimal precision (1 to 4).
Select Post-Hoc Test Method: Choose your desired multiple comparison method (e.g., Tukey HSD for all pairwise comparisons or Dunnett for control comparisons).
Set Control Level (if using Dunnett): Pick the designated control group level from the control level dropdown menu.
Execute Analysis: Click the bold Catalyze / Run Analysis button in the sidebar.
Review & Export: Switch between Main Summary Tables, Interactive Plots, and AI Text Interpretations. Download outputs in Excel (.xlsx), Word (.docx), PowerPoint (.pptx), or high-res chart image formats.

6. SAMPLE RESULTS & INTERPRETATION

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

How to Interpret the Output:

Lettering Codes (a, b, c): Treatment Condition_Alpha (Mean = 128.90, Letter = 'a') is significantly higher than all other conditions.
Shared Letters: 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 Difference: Control_Baseline (102.78, 'c') has the lowest mean and differs significantly from all active treatment conditions.

7. BEST PRACTICES & GUIDELINES

Post-Hoc Test Selection Guide

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).

Data Validation & Missing Values

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.

Cite DATES in Research Papers

If you use the DATES Mean Comparison module for experimental data analysis in published scientific research, please cite it as follows:

@software{dates_app_2026, author = {DATES Development Team}, title = {DATES: Data Analysis and Trial Evaluation System}, year = {2026}, url = {https://dates-app.org}, note = {Descriptive Statistics — Mean Comparison & Grouping Module} }

Mean Comparison & Grouping User Guide

1. OVERVIEW & OBJECTIVES

2. CONFIGURATION & PARAMETERS

3. INPUT DATA FORMAT REQUIREMENTS

4. STATISTICAL FOUNDATIONS & METRICS (PLAIN TEXT DEFINITIONS)

Group Mean

Standard Deviation (SD)

Standard Error of Mean (SE)

Pairwise Difference Estimate

Adjusted p-Value

Compact Letter Display (CLD)

5. STEP-BY-STEP WORKFLOW

6. SAMPLE RESULTS & INTERPRETATION

How to Interpret the Output:

7. BEST PRACTICES & GUIDELINES