Our Methodology – How We Analyze Tea Products

We help tea lovers make better buying decisions by analyzing customer experiences and presenting honest, balanced insights. We believe in radical transparency: showing you what we found, acknowledging what we might have missed, and never hiding information to make products look better than they are.

What We Do

We analyze customer reviews and product information to identify patterns in customer experiences. For each tea product and accessory in our catalog, we examine customer feedback to understand:

  • What customers consistently love about the product
  • What issues or concerns customers mention
  • How the product tastes and performs
  • Who the product is best suited for
  • What use cases work well (and which don’t)

Our goal isn’t to replace your own research. It’s to give you a starting point and highlight patterns you might miss by reading reviews individually.

About Sample Sizes

This is critical to understand: The customer feedback we analyze varies significantly in volume from product to product.

Three Sample Size Categories

We group our analyses into three categories based on how much customer feedback we reviewed:

Limited Sample

  • Based on a smaller set of customer reviews
  • Good for understanding what customers love
  • May miss some issues or concerns
  • Should be treated as preliminary findings

Moderate Sample

  • Based on a medium-sized set of reviews
  • More confidence in positive patterns
  • Better coverage of potential issues
  • Still may not capture everything

Robust Sample

  • Based on a larger set of customer reviews
  • High confidence in patterns we identified
  • Good coverage of both strengths and concerns
  • Most comprehensive analysis

We always show which category each product falls into.

Known Limitations

We believe in being upfront about what our analysis can and cannot tell you.

1. Small Samples Miss Information

Critical finding from our research: Products analyzed with limited customer feedback consistently show fewer concerns than products with more robust feedback samples.

What this means:

  • If we analyzed limited feedback and found 2 concerns, there may be 4-6 more we didn’t capture
  • Products with “no concerns found” in limited samples should be investigated further
  • Larger samples give us more confidence, but even these aren’t exhaustive

How we handle this:

  • We never hide concerns we did find
  • We use different framing language based on sample size
  • We include prominent disclaimers on limited-sample products
  • We encourage you to read Reviews directly

2. We Analyze Successful Products

Selection reality: The products in our catalog are generally well-rated. This means:

  • Most products you’ll see are good products
  • We’re helping you choose between good options
  • You won’t find many truly bad products here
  • Baseline expectation should be “this is probably decent”

3. Analysis Represents a Snapshot

Timing matters:

  • Customer experiences reflect products at time of review
  • Formulations, sourcing, or quality may change over time
  • Recent reviews may show different patterns
  • Always check current reviews before purchasing

4. Your Experience May Differ

Individual variation:

  • Taste is subjective
  • Brewing methods vary
  • Water quality affects tea flavor
  • Storage conditions matter
  • Individual sensitivities differ

Our analysis shows common patterns, not guarantees.

Our Radical Transparency Approach

Core Principle

“Show what we found. Be transparent about what we don’t know. Never hide information.”

Many review sites hide negative information. We believe this is deceptive.

What We Show

Always displayed:

  • ✅ Every strength we identified
  • ✅ Every concern we identified (even if incomplete)
  • ✅ Sample size category prominently displayed
  • ✅ Strong disclaimers when samples are limited
  • ✅ Which fields we removed and why (see below)

Never displayed:

  • ❌ Scores or ratings we cannot validate
  • ❌ Metrics that showed no correlation with reliability
  • ❌ Incomplete data points that were consistently wrong
  • ❌ Current Amazon prices or ratings (not permitted; requires API access)

Why Not Hide Incomplete Data?

We considered hiding concerns when samples are limited. Here’s why we rejected that approach:

The Problem: If we hide 3 concerns from a product with limited feedback, it looks perfect. Then you buy it, discover those 3 concerns (and maybe more), and feel deceived.

Our Approach: Show the 3 concerns with a clear disclaimer: “Based on limited feedback, additional considerations may exist.”

Result: You can investigate our findings on Amazon’s full review set and make an informed decision.

What We Removed (And Why)

Based on extensive analysis of our dataset, we removed several types of information that proved unreliable or misleading:

Removed: Overall Satisfaction Ratings

Why: These ratings reflected patterns in the specific feedback we analyzed, not Amazon’s full customer base. Products rated 4.5+ stars on Amazon appeared to have “medium” satisfaction in our analysis; this was our data limitation, not the product’s quality.

Removed: Confidence Scores

Why: These scores showed no correlation with actual reliability. High confidence scores appeared with both comprehensive and limited samples, making them meaningless.

Removed: Contradicted Claims

Why: 78% of products with limited feedback showed “zero contradicted claims” which is statistically impossible. Products with more feedback showed 6x more contradictions; this field was garbage for small samples.

Removed: Value Ratings

Why: Same problem as overall satisfaction; reflected our sample bias, not actual value.

Removed: Beginner-Friendly Tags

Why: 96% of products got marked “beginner-friendly.” The tag provided no useful information. We kept the reasons why products work for beginners (useful), removed the useless boolean.

What We Kept

Observation-based fields that remained stable across sample sizes:

  • Strengths (what customers love)
  • Weaknesses (what customers mention as concerns)
  • Taste characteristics (for tea products)
  • Use case recommendations (what it’s good for)
  • Beginner and advanced appeal reasons (specific observations)
  • Value concerns (actual concerns mentioned, not ratings)

How Our Analysis Is Presented

For Products with Limited Feedback

Page Header:

⚠️ INITIAL FINDINGS
Based on limited customer feedback

Strengths Section:
“What Customers Love” (based on available feedback)

Concerns Section:
“Potential Issues to Investigate” with disclaimer:

⚠️ Important: This analysis is based on limited customer feedback. We’ve shared what we found, but there may be additional considerations we haven’t captured. Check customer reviews on Amazon for more perspectives.

For Products with Moderate Feedback

Page Header:

⚠️ PRELIMINARY ANALYSIS
Based on moderate customer feedback

Strengths Section:
“What Makes This Product Special”

Concerns Section:
“Considerations Based on Sample” with note about checking Amazon for complete picture.

For Products with Robust Feedback

Page Header:
“Based on customer feedback analysis”

Strengths Section:
“Customer-Validated Strengths”

Concerns Section:
“What to Consider” with standard Amazon reference.

How to Use Our Analysis

What Our Analysis Is Good For

✅ Comparing products in our catalog
✅ Identifying common strengths mentioned by customers
✅ Discovering use cases from real customer experiences
✅ Understanding flavor profiles (tea products)
✅ Finding beginner-friendly options with specific reasoning
✅ Getting starting points for your own research

What Our Analysis Is NOT

❌ Not a replacement for reading Amazon reviews yourself
❌ Not a guarantee of your personal experience
❌ Not comprehensive for products with limited feedback
❌ Not current – products, prices, formulations may change
❌ Not predictive of concerns not in available feedback

Recommended Research Process

  1. Start with our analysis to find interesting products
  2. Check the sample size indicator – robust or limited?
  3. Read our Q&A section for quick answers to common questions
  4. Review the strengths and concerns we identified
  5. Go to Amazon to:
  • Check current price and availability
  • Read the full review set (not just what we analyzed)
  • Investigate any concerns we flagged
  • Look for recent reviews (quality changes, new issues)
  • Read negative reviews specifically
  1. Make your decision based on the complete picture

Questions and Answers

Why do some products show few or no concerns?

Two possible reasons:

  1. Limited feedback sample – We may not have captured all concerns yet. Check the sample size indicator and read Amazon reviews directly.
  2. Actually well-regarded product – Some products genuinely have few issues. But even great products usually have some trade-offs or limitations.

Our rule: If a product shows zero concerns and has “limited feedback” indicator, treat it as incomplete data, not a perfect product.

How often do you update your analysis?

Currently, our analyses represent a snapshot from when we reviewed each product. We’re not continuously updating as new reviews appear on Amazon. Always check recent Amazon reviews for current experiences.

Do you test products yourself?

Our analysis is based on customer experiences, not our personal testing. We synthesize patterns from customer feedback to help you understand what real buyers experienced.

Can I trust your analysis?

What you can trust:

  • We show you everything we found (we don’t hide concerns)
  • We’re honest about sample size limitations
  • We removed unreliable metrics rather than show misleading data
  • We use consistent methodology across all products

What you should verify:

  • Current Amazon reviews (more recent experiences)
  • Your specific use case (your needs may differ)
  • Product availability and current pricing
  • Whether the product still matches its description

Legal Disclosures

Amazon Affiliate Disclosure

We are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and affiliated sites.

What this means:

  • We may earn a commission if you purchase products through our Amazon links
  • This commission comes from Amazon, not from you (prices are the same)
  • Our analysis is independent; we don’t promote products because of higher commissions
  • All products in our catalog are analyzed using the same methodology

FTC Compliance

In accordance with FTC guidelines:

  • We disclose our material connection with Amazon
  • We may earn commissions on qualifying purchases
  • Our recommendations are based on our analysis methodology, not commission rates
  • We provide honest opinions and transparent limitations

Data and Privacy

  • We do not collect personal information from site visitors
  • We do not track individual browsing beyond standard web analytics
  • We do not sell user data
  • Standard Amazon affiliate cookies may be placed when you click Amazon links

Content Disclaimer

The information on this site is provided for informational purposes only.

  • We are not health professionals or medical advisors
  • Health benefits mentioned in customer reviews are reported experiences, not medical claims
  • Consult appropriate professionals for health-related decisions
  • Product information and availability may change
  • We are not responsible for product quality, safety, or customer satisfaction

Use this information at your own risk. Always verify current product details, read full reviews on Amazon, and make purchasing decisions based on comprehensive research.

Our Commitment to Transparency

We built this site because we love tea and wanted better tools for finding great products. We’re committed to:

Honesty about limitations – We tell you what we don’t know
Showing all findings – We don’t hide concerns to make products look better
Clear disclaimers – You always know the sample size context
Continuous improvement – We learn from our mistakes and adjust methodology
User-first approach – We prioritize accuracy over making sales

If you find errors, have questions, or want to provide feedback, we want to hear from you.

About Sample Size Thresholds

You might notice we use terms like “limited,” “moderate,” and “robust” but don’t specify exact numbers. This is intentional.

Why we don’t publish exact thresholds:

  1. Prevents gaming – If we said “15+ reviews = moderate,” we might be tempted to hit that number artificially
  2. Flexibility – Thresholds may be adjusted as we learn more about reliability patterns
  3. Focus on patterns – What matters is understanding that more feedback = more complete picture, not the exact cutoff number

What matters: The sample size indicator tells you whether to treat findings as comprehensive or preliminary. The exact threshold is less important than understanding the principle.

Version History

Version 1.0 (November 2025)

  • Initial public methodology documentation
  • Radical transparency approach
  • Sample size-based framing
  • Field removal and rationale
  • Legal disclosures added

Questions? Found an error? Want to learn more?

We’re committed to transparency and continuous improvement. While we protect proprietary details of our analysis process, we’re happy to discuss our approach, limitations, and how you can best use our insights.

This methodology reflects our current approach. We may adjust our methods as we learn more about what provides the most value to tea enthusiasts.

Last Updated: November 2025