Why and how you can promote more students become statisticians

04/21/25 • 29 min

The Effective Statistician - in association with PSI

Interview with Emma Crawford and Alex Spiers

✔ Why STEM outreach is so important for the future of our industry

✔ How your company, community, and even your own career can benefit from getting involved

✔ The challenges students face (especially girls and those from underserved areas) in seeing themselves in STEM

✔ How PSI’s Schools Outreach team can support you—with ready-made materials, training, and volunteer opportunities

✔ What it's really like to walk into a classroom and talk about your job

Why You Should Listen:

You don’t need to be a teacher to make an impact. If you’ve ever wished more people knew how rewarding our work is, this episode will show you how to be part of the solution. Whether you join a virtual session or speak at a local school, your story might be the spark that changes a student’s life.

Get Involved – Here's How

Whether you're based in the UK or elsewhere, there are so many ways to participate:

Visit the PSI Schools Outreach Hub
Includes career talk templates, outreach resources, and volunteering info
Contact the PSI team: [email protected]
Join platforms like:
- STEM Learning
- I'm a Scientist
- Maths4Girls
If you're a PSI member, sign up for the Schools Outreach Newsletter in your Glue Up profile ("Outreach Champions Distribution List")

You can also download editable school letter templates, contribute your own materials, or join our resource-sharing library to support other members doing outreach.

Why You Should Listen:

Get Involved – Here's How

Whether you're based in the UK or elsewhere, there are so many ways to participate:

Visit the PSI Schools Outreach Hub
Includes career talk templates, outreach resources, and volunteering info
Contact the PSI team: [email protected]
Join platforms like:
- STEM Learning
- I'm a Scientist
- Maths4Girls
If you're a PSI member, sign up for the Schools Outreach Newsletter in your Glue Up profile ("Outreach Champions Distribution List")

You can also download editable school letter templates, contribute your own materials, or join our resource-sharing library to support other members doing outreach.

Previous Episode

Clarifying confusions around interim, primary, final, and other analyses in clinical trial

Interview with Kaspar Rufibach

What You’ll Learn in This Episode:

✔ The true definitions of interim, final, primary, confirmatory, and updated analyses

✔ Why a “final analysis” doesn’t always mean “last analysis”

✔ How language choice can impact stakeholder trust and regulatory interpretation

✔ The operational vs. statistical meaning of “stopping a study”

✔ When to use which data cuts and how to define your analysis set

✔ The nuances of estimation vs. hypothesis testing after stopping a trial

✔ Why clarity in communication is just as critical as technical accuracy

Why You Should Listen:

Even experienced statisticians often use terms like “final” or “interim” inconsistently, which can cause confusion not just within study teams, but also with regulators, clinicians, and the public.

Kaspar provides much-needed clarity on how we as statisticians can:

Use more precise terminology when describing study analyses,
Align our language with stakeholders,
Avoid miscommunication that can erode public and regulatory trust, and
Educate others on the importance of estimating vs. testing.

Resources & Links:

🔗 LinkedIn article by Kaspar Rufibach on clinical trial

🔗 Preprint Paper (with industry and regulatory co-authors)

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 Medical Data Leaders Community – Join my network of statisticians and data leaders to enhance your influencing skills.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

Join the Conversation:
Did you find this episode helpful? Share it with your colleagues and let me know your thoughts! Connect with me on LinkedIn and be part of the discussion.

Subscribe & Stay Updated:
Never miss an episode! Subscribe to The Effective Statistician on your favorite podcast platform and continue growing your influence as a statistician.

Next Episode

RWE and JCA - how do they go together

Interview with Katrin Kupas

What You’ll Learn in This Episode:

✔ What the JCA is and why it changes how we plan evidence generation

✔ When and how RWE can help answer comparator questions in HTA

✔ The risks of non-randomized comparisons—and how to mitigate them

✔ Why we need an integrated evidence plan early in development

✔ How tools like ROBINS-I and quantitative bias analysis can improve credibility

Why You Should Listen:

If you’re a statistician, medical affairs lead, or part of an HEOR or market access team, this episode will help you:

✔ Develop a stronger evidence generation plan across the drug lifecycle

✔ Understand how real world evidence can support JCA submissions

✔ Learn when RCTs aren’t enough—and how RWE can fill the gap

✔ Gain practical advice for designing indirect treatment comparisons

✔ Improve your bias assessment strategies with tools like ROBINS-I

Resources & Links:

🔗 ROBINS-I Tool – for evaluating bias in non-randomized studies

🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.

🔗 Medical Data Leaders Community – Join my network of statisticians and data leaders to enhance your influencing skills.

🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.

If you’re working on evidence generation plans or preparing for joint clinical advice, this episode is packed with insights you don’t want to miss.

Join the Conversation:
Did you find this episode helpful? Share it with your colleagues and let me know your thoughts! Connect with me on LinkedIn and be part of the discussion.

Subscribe & Stay Updated:
Never miss an episode! Subscribe to The Effective Statistician on your favorite podcast platform and continue growing your influence as a statistician.