This installment of Yale's 'Understanding Medical Research: Your Facebook Friend is Wrong' looks at how the timing of measurements can distort a study's conclusions. The lecturer walks through immortal time bias, lead-time bias, and length-time bias, showing how each can make a treatment or screening test look more effective than it actually is simply because of when patients enter or are counted in a study. Using examples drawn from cancer screening and survival statistics, the talk explains why a longer apparent survival time after diagnosis does not always mean a treatment works, since earlier detection alone can inflate the numbers. The lecture is short and focused, aimed at giving viewers a concrete checklist for spotting these timing traps when they read health headlines or research summaries, rather than a broad survey of research design.