Evaluating Scientific Studies
Go beyond sensational headlines to determine whether research findings actually hold up under scrutiny. Learn how to interrogate experimental controls, diagnose analytical bias, and distinguish genuine discoveries from statistical noise.
Like Duolingo, but for Evaluating Scientific Studies. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
A short one: 12 levels across 2 sections, about 24 minutes end to end, roughly 5 days at five minutes a day. It moves through Auditing Study Design and Data and Detecting Bias and Fragility. It assumes you already know the basics.
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Key ideas in Evaluating Scientific Studies
- Inert placebos unblind participants when the experimental treatment produces noticeable side effects
- An active placebo mimics noticeable side effects without providing the experimental therapeutic benefit
- Preserving procedural expectations requires matching the experiential cues across both study arms
- A confounder correlates with both exposure and outcome to generate an illusory statistical link
- Stratification analyzes data within homogenous subgroups to reveal the true underlying pattern
- Matching balances known confounding traits across cohorts prior to measuring the final outcome
- Self-sorting lets unmeasured baseline traits skew which participants enter each experimental arm
- Differential attrition occurs when dropouts leave one arm disproportionately healthier than the other
- True baseline equivalence is disrupted whenever participation or retention correlates with the outcome
- Researcher expectancy reliably introduces measurable confirmation bias into subjective clinical grading
- Assessor blinding ensures the person scoring the outcome does not know who received the treatment
- Even subtle diagnostic cues can lead unblinded evaluators to over-interpret slight improvements
- Natural experiments leverage arbitrary policy thresholds or borders that assign exposure as if at random
- Administrative cutoffs allow causal inference when randomized controlled trials are unethical
- Comparing subjects right around an arbitrary boundary neutralizes environmental confounders
- A p-value assumes the null hypothesis is true and calculates the probability of seeing data that extreme.
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Evaluating Scientific Studies the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
Imagine testing a pill that tingles. How can researchers keep volunteers from guessing who got the real medicine?
Get it right to open this lesson and 11 more in the app.
Where Evaluating Scientific Studies takes you
- 1
Auditing Study Design and Data
- Testing the Integrity of Controls
- Weighing Effect Size and Power
- 2
Detecting Bias and Fragility
- Spotting P-Hacking and Selective Reporting
- Assessing Generalizability and Replication
2 sections · 4 units · 12 levels. Built to play, not to enroll.
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