This blog offers a framework for making responsible Proof-of-Concept decisions when budgets limit sample size, showing how adaptive thinking replaces rigid historical dogma.

A dog may follow commands without understanding why, but in science, blindly following ‘statistical’ rules without grasping the principles can lead to cherry picking, misinterpretation, and compromised integrity.

When diagnostic observations are presented as facts, the consequences ripple through public perception, funding, and the progress of science itself.

In last week's blog, I showed that the Frequentist/Bayesian divide is largely philosophical, with practical differences between approaches being limited. Even unique Bayesian methods can often be interpreted in a frequentist manner. Yet, adoption of these methods, dubbed Bayesian, even when appropriately applied, has been slow in drug development. This article explains why.

Despite the tribal divide, Bayesian and Frequentist methods produce nearly the same action in the preponderance of situations.

A reflection on how a childhood toy, civic pride, and a one-page impact statement taught me how to lead without breaking under pressure.

Although regulators fear overly optimistic results, the real consequences of pre-disclosure fall primarily on sponsors, increasing variability and risking missteps in trial conduct and decision making.

Systems meant to protect trial integrity sometimes devolve into ritual, adding bureaucracy without improving patient safety or science.

The best statistical solutions in drug development are not always conventional, but they are always grounded in the real scientific and operational question.

A personal tale of love, compromise, and curiosity that led to an unexpected but fulfilling career in statistics, and how you can find your own path too.

Using the example of estimating proportions from continuous data, this essay highlights a general lesson in statistics: the non-obvious choice is often the most effective, whether in analysis, research, or decision-making. This should not be a surprise, since the same is true in life.

Through stories of name confusion and statistical jargon, the post emphasizes the importance of precise communication in statistics.

This blog untangles the difference between measurement precision and trial efficiency in the era of digital health.

By examining parallels between youthful political beliefs and professional lessons in pharma, the essay demonstrates that impractical, intractable, and shocking outcomes often arise from extremes.  This leads to the conclusion that flexibility is key to innovation.

Whether you lead a baseball team or a statistics bureau, your career can hinge on results you don’t fully control. This piece explores two high-profile examples and extracts three timeless rules for leaders: know what’s working, fix what’s broken, and make sure those above you know the difference.

This essay warns that unplanned interim analyses, like walking out of a movie before its twist, mislead decision-making and fuel avoidable failures in drug development.

This is the story of a statistical method dismissed by many, embraced by me, and why it’s time we judge tools by what they’re for, not what they’re not.

A humorous and reflective look at how statisticians are perceived in relationships, ethics, and popular media, with a call to raise their profile as heroes rather than grim messengers.

A tale of two subgroups, one that failed and one that changed a label, revealing why logic, not just math, must drive subgroup thinking.

A call to rethink our regulatory interpretations, especially in early-phase research, and to refocus on patient impact, not just procedural perfection.