Hypothesis Testing
Hypothesis Testing is the scientific method applied to business. You form a hypothesis ("Shorter calls convert better"), test it with real data, and determine if results are statistically significant or due to chance. This approach eliminates guesswork from decision-making.
What Is Hypothesis Testing in Business?
Rather than relying on intuition, hypothesis testing formalizes your assumptions and tests them. A/B testing is the most common form: you change one variable (email subject line, call greeting, chat response time), measure the outcome, and determine if the change actually improved results or was just luck. Analyzing conversation variations lets you test different agent approaches to find what truly converts.
Statistical Significance vs. Lucky Results
If 10 people use version A and 8 convert (80%) vs. version B where 9 convert out of 20 (45%), which is better? Looks like A. But with small samples, this could be luck. Statistical significance tests whether results would repeat with larger samples, or if they're just random noise.
Running Effective Hypothesis Tests
Key elements:
- Clear Hypothesis: "Changing greeting from 'Hi' to 'Hello' will increase call conversion by 5%"
- Control Group: Keep one version unchanged as baseline
- Sample Size: Test with enough customers to achieve statistical significance (typically 30+ per group)
- One Variable: Change only one thing at a time—changing multiple variables makes it impossible to know what worked
- Time Window: Run tests long enough to account for daily/weekly variations
FAQs related to
Hypothesis Testing
How long should I run an A/B test?
Until you reach statistical significance with your sample size (typically 2-4 weeks). Running longer doesn't hurt but is unnecessary. Running shorter risks false positives (thinking something works when it's luck).
What can I test to improve conversion?
Nearly everything: call greeting, response time, qualifying questions, objection-handling approach, offer structure, follow-up timing. Conversation data reveals which variations successful agents use most—those are your highest-potential tests.
What if my test shows no difference?
That's valuable information too. If changing your approach didn't improve results, keep the simpler version (less effort for same results). Move on to test something else. Every negative test accelerates learning.
Can I test multiple things at once?
Not if you want to know what worked. Testing 5 variables simultaneously makes results uninterpretable. Even if conversion improves, you won't know which of the 5 changes caused it. Test one variable per hypothesis.
How do I know if a result is real or luck?
Use statistical significance calculators (online tools exist for free). A result is "significant" if it would occur by chance less than 5% of the time. Use a significance calculator with your sample size and conversion rates to confirm.