Statistical analysis of brews

Why Use Statistics?

• Coffee brewing produces natural variability.
• Statistics help determine whether observed differences are real effects or just noise.
• Provides confidence in recipe development, QC, and research.

Common Statistical Tools

1. Descriptive Statistics:

• Mean, median, standard deviation.
• Used for TDS, EY, or brew time averages.

2. Comparative Tests:

• t-test: Compare two recipes (e.g., 92°C vs 96°C water).
• ANOVA: Compare multiple recipes or grind levels.

3. Correlation & Regression:

• Show relationships between variables (grind size vs EY).
• Build predictive models.

4. Sensory Statistics:

• Triangle test significance.
• Panel calibration and inter-rater reliability.

Practical Example

• Test: Does increasing dose improve sweetness in espresso?
• Method: Brew 3 dose levels (18g, 19g, 20g), measure TDS + sensory scores.
• Analysis:
• Use ANOVA to test differences in TDS and sweetness scores.
• Apply post-hoc tests to see which doses differ.
• Outcome: Identify optimal dose range with statistical support.

Benefits in Coffee Research

• Reduces subjective bias by quantifying results.
• Guides recipe adjustments with evidence.
• Strengthens credibility when publishing findings or training staff.

Tips for Baristas & Roasters

• Keep sample sizes reasonable (≥3–5 repetitions).
• Use simple spreadsheet tools for calculations.
• Combine sensory and instrumental data for robust conclusions.

Summary

Statistical analysis of brews moves coffee experimentation from intuition to data-driven confidence. By applying tests like t-tests, ANOVA, and regression, professionals can validate recipes, minimize variability, and achieve greater consistency and clarity in brewing outcomes.

• Tags:
• Grind Size
• Coffee Brewing
• Tds Ey
• Baristas Roasters
• Measure Tds
• Training Staff
• Data Driven
• Variables Grind
• Coffee Research
• Practical Example
• Recipe Development