Curve Shifts Don’t Lie, But Your Eyes Might
- Terry's Desk
- Sep 2
- 4 min read
If you’ve ever squinted at two curves and thought, “That looks different… I think”, you already know the trap.
In drug discovery, intuition creeps in. Eyes deceive. And when decisions ride on subtle shifts, “looks different” isn’t good enough.
You don’t need sharper eyesight. You need statistical methods that tell you, with confidence, whether the effect you’re seeing is real or just noise.
This session gives you exactly that. By the end, you’ll know how to choose the right test, design experiments with power built in, and validate results against the standards that matter. No more guesswork, only decisions you can trust.
In This Session, You’ll Gain:
✅ The ability to calculate how many replicates you really need for 95% confidence
✅ Tools to choose the right statistical test for means, curves, or linear responses
✅ A framework for validating whether your results match literature standards—or diverge in meaningful ways
From Guesswork to Numbers
You’ve run your experiment. Two data sets sit in front of you. They look different. But are they?
The first trap in drug discovery is relying on visual inspection. Scatter can masquerade as signal. Subtle shifts vanish in noise. What you need is a number that tells you whether those differences are real.
That’s where t-tests come in. By comparing two means (paired or unpaired) you’ll learn whether the divergence is genuine or whether random variation explains it.
No more arguments about “what the graph seems to say.” You’ll move from “I think” to “I know.”
And once you understand the logic, you’ll see how it scales. Two groups? t-test. Three or more groups, or multiple variables creeping in? That’s where ANOVA steps up. Same principle, broader reach.
How Many Replicates Do You Really Need?
The second trap is habit. Most labs default to “n=3” or “n=6.” But those numbers don’t come from rigor; they come from tradition.
With power analysis, you’ll stop guessing. Instead, you’ll calculate exactly how many replicates are needed to detect the differences that matter, at the confidence level you demand.
For high-throughput screens where sensitivity isn’t critical, you’ll save time by running fewer replicates.
For critical experiments where tiny differences mean millions of dollars, you’ll ensure enough replicates to trust your decision.
Once you learn to design with power analysis, you’ll never run blind again.
When Curves Shift
Some of the most difficult calls in pharmacology happen when two curves look slightly shifted. Is it a real pharmacological effect, or just noise?
Here’s where the F-test becomes indispensable. Instead of relying on debate—or worse, intuition—you can calculate an F-value that objectively shows whether the two curves belong to one population or two.
Real curve shifts? Captured.
Artifacts? Dismissed.
This ability transforms meetings from endless discussions to clear decisions.
Straight Lines, Slopes, and Systems
Not all data bends into curves. Sometimes you’re working with linear fits: Schild plots, dose ratios, regression models. Here, the question is whether two lines describe the same system, or whether treatment has shifted slope or intercept.
Enter ANOVA (Analysis of Variance). It gives you the power to compare slopes and elevations statistically—so you can confirm whether your system is consistent or diverging in meaningful ways.
What used to be a gray area—“maybe these lines are different?”—becomes a black-and-white decision.
Does Your Assay Agree With the Literature?
Your experiment gives a pKB of 8.0. The literature says 7.5. Is that consistent, or is your assay biased?
With confidence intervals and modified t-tests, you’ll be able to:
Confirm whether your mean truly overlaps with accepted values
Verify if binding and functional assays deliver consistent results
Decide with confidence whether a new method is interchangeable with existing ones
Instead of vague comparisons, you’ll have statistical clarity on whether your assay is valid or drifting.
What You’ll Walk Away With
By the end of this session, you won’t just “know about statistics”, you’ll know how to use them to sharpen every discovery decision. Specifically, you’ll be able to:
Design experiments with the right replicate count to balance rigor and efficiency
Choose the correct statistical test for your data structure
Compare curves and lines without guesswork
Validate your results against standards and across methods
Trust that your conclusions reflect reality, not bias
This is more than learning tools. It’s learning how to design, analyze, and decide with confidence built in.
Statistical Methods in Drug Discovery: Your Edge
If you’re still making calls by sight, habit, or tradition, you’re leaving risk on the table. With the right methods, you’ll know (not hope) that your results are real, reproducible, and actionable.
This isn’t just another lecture. It’s a shift in how you approach discovery. And once you make that shift, you’ll never go back.
Why Terry’s Corner
Your work demands clarity, speed, and confidence, especially when early decisions determine which programs move forward. Terry’s Corner is designed to give you the edge.
Here, you’ll find:
Weekly lectures that sharpen the tools you actually use in discovery
A growing on-demand library of lessons you can revisit whenever you need them
Exclusive access to the next AMA session
Direct engagement opportunities through AMAs and topic suggestions
Practical insights distilled from decades of pharmacology experience
Whether you’re validating assays, refining models, or deciding which leads to advance, Terry’s Corner ensures you’re working with proven strategies, not assumptions.
Stay current. Stay confident. Stay ahead.
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