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Statistically-grounded

Describing conclusions, analyses, or decisions that are based on and supported by sound statistical methods, data analysis, and the principles of probability. A statistically-grounded approach ensures that findings are not based on mere chance or subjective interpretations, but rather on rigorous evaluation of data and its inherent uncertainties. It emphasizes the importance of quantifiable evidence and the application of appropriate statistical tests to validate claims and make informed judgments. Statistical grounding provides a framework for drawing objective and reliable conclusions, minimizing the influence of bias and promoting transparency.

Statistically-grounded meaning with examples

  • The researcher's conclusions were statistically-grounded, based on a large-scale survey and rigorous analysis using regression models. This ensured that the observed correlations between variables were not merely coincidental but had genuine statistical significance. The methodology was transparent, allowing other researchers to replicate the study and verify the findings.
  • Building a statistically-grounded business strategy required a deep dive into market data and customer behavior analysis. Through the use of predictive modeling and forecasting techniques, they were able to identify high-potential growth areas and refine their marketing campaigns. The data driven decision-making helped them minimize risks.
  • To assess the effectiveness of the new treatment, the medical team designed a statistically-grounded clinical trial, carefully controlling for confounding variables and using appropriate statistical tests. This enabled them to determine the likelihood that the observed improvement in patients was due to the treatment itself and not random chance or placebo effect.
  • The financial analyst provided a statistically-grounded risk assessment of the proposed investment. He employed Monte Carlo simulations to model various scenarios and estimate the potential range of outcomes. The robust methodology provided a reliable foundation for investment decisions, minimizing potential losses

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