How confident can you be in your market research results?

Statistical significance testing is a cornerstone of data-driven decision-making in market research. It helps determine whether your insights are due to actual differences or just random chance. But understanding confidence levels is a key piece of significance testing and can help you choose the right level of rigor for your research and avoid misinterpreting the data.

In this post, we’ll break down what a confidence level is, how to choose the right one, and how Suzy’s platform gives you the tools to run smart, flexible analysis.

Table of contents

• What is a confidence level?
• Confidence Level vs. Statistical Power
• When should you use a higher confidence level?
• Can you ever reach 100% confidence?
• When is it OK to use a lower confidence level?
• How do you choose the right confidence level?
• Confidence levels and statistical significance at Suzy
• Suzy Speaks: Confidence in open-ended analysis
• TL;DR
• Want to see confidence levels in action?

1. What is a confidence level?

A confidence level is the probability that your sample results reflect the true population values. In simpler terms, it tells you how sure you can be that your findings are not due to random chance.

The most commonly used confidence level is 95%, which means there's only a 5% chance your results are incorrect due to random variation. However, the right level depends on your research goals, sample size, and tolerance for risk.

Quick Example: If your concept test shows a preference for Concept A over Concept B at 95% confidence, there's a 95% chance that preference exists in the broader population.

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2. Confidence Level vs. Statistical Power

While confidence level tells you how certain you are about your results, statistical power refers to the likelihood that your test will detect an effect when one truly exists. Power increases with larger sample sizes and bigger effect sizes.

The Trade-Off: Higher confidence levels typically require stricter criteria, which can reduce power. It’s about finding the right balance between avoiding false positives (confidence level) and catching real differences (power).

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3. When should you use a higher confidence level?

Use a higher confidence level (e.g., 99%) when the stakes are high—like making a large investment or launching a national campaign. The higher the level, the lower your risk of drawing a false conclusion from random noise in the data.

Think high risk = high confidence.

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4. Can you ever reach 100% confidence?

Unfortunately, 100% confidence is impossible in statistical testing. There’s always the potential for sampling error, bias, or data anomalies. Even with perfect execution, uncertainty is a natural part of research.

Instead of aiming for perfection, aim for practical confidence—a level that aligns with your business goals and decision-making risk tolerance.

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5. When is it OK to use a lower confidence level?

Lower confidence levels (e.g., 90%, 85%, or even 80%) may be appropriate when:

• You're in an early-stage exploratory study
• You’re testing broad directional trends
• The decision carries low business risk
• You want to capture weaker signals that might warrant further testing

Lower confidence doesn’t mean your results are bad—it just means you’re accepting more uncertainty to explore more possibilities.

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6. How do you choose the right confidence level?

It comes down to your research context and goals. Here's a simplified guide:

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The best researchers choose their level strategically based on the trade-off between rigor and actionability.

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Confidence levels and statistical significance at Suzy

Suzy makes interpreting statistical significance seamless by automating confidence level testing across your research—helping you move faster and with greater clarity. You have the flexibility to view results tested at varying confidence levels, from 80% to 95%, depending on the depth and rigor your project requires. Try our intuitive statistical significance calculator below and learn more about the analysis tools we offer.

Where Suzy truly shines is in how its advanced tools surface confidence in meaningful, actionable ways:

Suzy Speaks: Confidence in open-ended analysis

Suzy Speaks enables qual insights at the scale of quant, so you can identify statistically significant insights across emergent themes and keywords. It surfaces statistically significant patterns in open-ended answers, giving you confidence in the themes and sentiments that emerge.

Whether you're validating a concept, analyzing verbatims, or testing messaging, Suzy Speaks ensures that qualitative insights carry the same statistical rigor as your quantitative work. You can quickly identify which ideas resonate most—and with which segments—backed by confidence-based analysis that guides next steps.

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TL;DR

1. Confidence level reflects how certain you are that your results are real, not chance.
2. Power and confidence are related but distinct; choose the right balance for your goals.
3. Higher confidence levels are best for high-stakes decisions.
4. 100% certainty is impossible, but you can still be highly confident.
5. Lower confidence levels are fine for exploration or early-stage testing.
6. Suzy gives you flexible, automated confidence level testing with advanced tools like Suzy Signals and Suzy Speaks to bring insights into sharper focus.

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Want to see confidence levels in action?

Book a personalized demo with the Suzy team to see how you can run tests at multiple confidence levels, visualize significance automatically, and act on real-time insights with confidence.

Schedule a demo.