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When a P-Value Is Misleading: What to Look for Instead

In clinical research, few problems cause more hidden damage than confounding. It distorts treatment effects, creates false associations, and leads investigators to conclusions that are statistically valid, but scientifically wrong. What makes confounding especially dangerous is that it often remains invisible until long after the paper is published. Fortunately, you do not need advanced modeling to detect it. Many of the most powerful warning signs of confounding appear long

Just like flicking a light switch, OpenSimplify lets you illuminate your data in an instant, no steep learning curve, no buried code, just insights on demand.

Discover how OpenSimplify helps researchers go from raw data to publication-ready manuscripts, automatically generating tables, summaries, and literature comparisons to streamline the publishing process.

We are excited to announce that OpenSimplify’s MyAnalyst (Clinical Reports v1.2.0) was recently cited in a peer-reviewed clinical study conducted by researchers at Albert Einstein College of Medicine. The paper, Clinical Features and Surgical Outcomes in Patients with Symptomatic Carotid Artery Webs, explored risk factors and surgical outcomes using advanced statistical modeling. And they used MyAnalyst to get there.