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Type 1 and type 2 errors are the two ways in which a hypothesis test can reach a wrong conclusion. A type 1 error is a false positive, concluding that an effect exists when in truth it does not, and its probability is set by the significance level, conventionally 0.05. A type 2 error is a false negative, failing to detect an effect that genuinely exists, and its probability is the complement of the study's statistical power. The two are in tension, since reducing the risk of one, for a fixed sample size, tends to increase the risk of the other, so study design balances them through the choice of significance level and, crucially, an adequate sample size to keep both risks acceptably low. Understanding these errors is essential to designing studies and to interpreting their results.

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NHS Drug Tariff Submission Support

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Value Proposition Development for MedTech

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Stakeholder Engagement for Health Technologies

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Sustainability and NET Zero for MedTech

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Evidence Generation and Management

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Dashboard Tools for Market Access

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MedTech Sales Team Empowerment

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NHS MedTech Business Case Formulation

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