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Forget tidy charts that “prove” the obvious. We open with a stork-baby correlation that looks convincing on a blackboard and use it to expose how clean numbers can hide messy truths about electric vehicles, insurance risk, and the way headlines get written. From there, we dig into the mechanics of data interpretation: why new registrations aren’t the same as sales, how identical EV datasets can support both bullish and bearish narratives, and what goes wrong when yesterday’s models are used to predict tomorrow’s road.

Our journey moves from market stats to real-world risk. Early EV insurance looked cheap because cautious first adopters and fewer young drivers skewed the data. Then AXA stress-tested assumptions, flagged higher accident likelihood in specific scenarios, and named a behaviour many drivers recognise: “overtapping” during brisk starts. Layer on battery pack vulnerability, scarce repair capacity, stringent isolation protocols, and upside-down salvage economics, and you get a claims picture that legacy ICE data could never forecast. The result is a sober look at why premiums rose and what has to change for costs to fall.

We close with a masterclass in survey framing via a dentist endorsement campaign that allowed multiple recommendations, creating a headline-ready “90%” without real differentiation. The thread tying it all together is context: definitions, lags, behaviour, incentives, and experimentation. When systems are stable, data sings; when technology and habits shift, experiments lead and datasets follow. If you care about EV adoption, insurance fairness, and honest communication, this conversation gives you the tools to spot manipulation and demand better questions.

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