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Hypothesis testing is a statistical procedure for assessing whether observed data are consistent with a specified null hypothesis, typically one of no effect or no difference, against an alternative. A test statistic is calculated and compared with what would be expected under the null hypothesis, yielding a p value that indicates how compatible the data are with it; if the p value falls below a pre-specified significance level, the null hypothesis is rejected. The procedure is subject to two kinds of error, wrongly rejecting a true null hypothesis (type 1) and failing to reject a false one (type 2), whose probabilities are governed by the significance level and the study's power. Its conclusions concern the role of chance and must be interpreted alongside effect sizes and confidence intervals, since statistical significance does not by itself establish clinical importance.

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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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MedTech Sales Team Empowerment

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NHS MedTech Business Case Formulation

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