Statistical-based

Statistical-based describes something that relies on or is derived from statistical analysis, data, and methods. It signifies that the information, conclusions, or decisions are grounded in numerical evidence, probabilities, and quantifiable relationships rather than solely on intuition, opinion, or anecdotal observations. The core of this approach involves the rigorous application of statistical principles to interpret and analyze data, identify trends, and make predictions with a certain level of confidence. Furthermore, the interpretation of 'statistical-based' hinges on the specific context of its application, spanning a diverse range of fields. It emphasizes the objective and data-driven nature of the conclusions made, highlighting that the conclusions are supported by calculations rather than conjecture.

Statistical-based meaning with examples

• The company's marketing strategy was statistically-based, utilizing A/B testing and market research to identify the most effective advertising campaigns. They meticulously tracked click-through rates, conversion data, and customer demographics. The adjustments in their approach, influenced by the statistical findings, were carefully made to maximize return on investment and achieve the intended reach, targeting only relevant demographics.
• This medical study's conclusions are statistically-based, providing strong evidence for the efficacy of the new drug. Researchers used robust statistical methods, controlled for variables, and meticulously documented the experimental procedure. The data obtained during clinical trials showed a clear statistical difference between the treatment and control groups, making it reliable to draw conclusions on its medical use.
• The economic forecast is statistically-based, incorporating historical economic indicators, trends, and sophisticated econometric models. The analysts utilized regression analysis, time series analysis, and other complex algorithms to predict future economic performance. The validity of this forecast relies heavily on the quality and relevance of the data inputs, and the assumptions that underpin the models used.
• The risk assessment of the new project was statistically-based, involving an analysis of various failure probabilities and potential losses. Experts used Monte Carlo simulations, calculating the expected value and variance of potential outcomes. The conclusions were therefore used to develop mitigation strategies and contingency plans, informed by quantifiable measures of risk and uncertainty for future events.