TRUST Research Center

Bias can silently undermine your research ⚠️

This infographic highlights the **main sources of bias in observational studies**:
🔹 Selection Bias (who gets included)
🔹 Information Bias (how data is collected)
🔹 Confounding & Interaction (hidden variables)
🔹 Detection & Time Biases (when and how outcomes are measured)

💡 It also shows practical strategies to **prevent and adjust for bias** during study design, data collection, and analysis.

Because strong research is not just about results — it is about how reliable those results are.

📩 Want to confidently identify and handle bias in your studies? join our upcoming course:

👉 https://wa.me/201119678899?text=I%20want%20to%20join%20the%20Observational%20Studies%20course

📊 **Struggling to choose the right statistical test? Start here.**

The **Unpaired t-test** is one of the most essential tools in clinical research—but many people use it without fully understanding *when*, *why*, and *how*.

This infographic simplifies it for you:

🔹 **When to use it?**
To compare the means between two independent groups (e.g., Drug A vs Drug B)

🔹 **Key idea:**
You’re comparing **signal (mean difference)** to **noise (standard error)** → giving you the *t-value*

🔹 **Critical insight:**
Small samples = more uncertainty → higher critical values → heavier tails (that’s why we use the t-distribution, not Z)

🔹 **What most people miss:**
✔ Checking variance (equal vs unequal) changes the calculation
✔ Reporting is not just p-value — you need CI + effect size (Cohen’s d)

🔹 **Real-world takeaway:**
Statistical significance is not enough—interpret magnitude and clinical relevance.

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💡 If you want to **master statistics in a practical, clinician-friendly way**, join our upcoming **Statistics for Clinicians (S4C)** course. click on the following link and click send:
https://wa.me/201119678899?text=I%20want%20to%20join%20the%20Statistics%20for%20Clinicians%20course

🎯 What’s the real meaning of “statistically significant”?

In biological research, variability isn’t a flaw—it’s the rule. Even when patients receive the *same* treatment, their responses can vary widely: some improve dramatically, others moderately, and some may not improve at all.

So here’s the reality:
We can *never* be 100% certain in our conclusions.

👉 That’s why researchers made a “deal”:

✔️ Every study must calculate the probability of error (using statistical tests)
✔️ The scientific community agrees to take results seriously only if this error is ≤ 5%

This 5% threshold is what we call alpha (α) — the acceptable risk of being wrong.

And the value we calculate in each study?
That’s the p-value.

💡 So what does “statistically significant” actually mean?
It simply means:
➡️ The p-value should respect alpha (0.05)

📌 If p ≤ 0.05 → Statistically significant
📌 If p > 0.05 → Not statistically significant

No magic. No mystery. Just a practical agreement to manage uncertainty in science.

🎥 Watch the full video: https://www.youtube.com/watch?v=QTzrL7P1Uxo to understand this concept in a simple, intuitive way.

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🚀 Starting in 10 Days! Don’t Miss Out

Our course “Validity, Reliability & Diagnostic Accuracy Measures” is starting in just 10 days.

If you’re aiming to strengthen your research skills and confidently handle statistical concepts, this course will take you step by step through:
📊 Validity & reliability
📈 Questionnaire validation
📉 Agreement measures
🔍 Diagnostic accuracy analysis

Whether you're a beginner or looking to refine your understanding, this course is designed to make complex concepts clear and practical.

📩 Get full course details here:
https://wa.me/201119678899?text=Validity+Reliability+course+details

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