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This is a thoughtful and important reflection on how we interpret data, especially during global crises like the pandemic. The stark difference between reported COVID-19 death rates in China and the U.S. does raise legitimate questions—not necessarily about blame, but about transparency, methodology, and context. Data doesn’t just appear in a vacuum; it’s collected, filtered, and reported based on various political, cultural, and logistical factors. Blindly trusting numbers without understanding how they were gathered can lead to misleading conclusions. It’s also worth considering how access to testing, definitions of “COVID death,” and public health responses varied wildly between countries. This isn’t about conspiracy—it’s about critical thinking. We should always approach statistics with a balance of skepticism and context, especially when the data seems unusually clean or convenient.

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