Statistical analysis aims to quantify uncertainty and express the likelihood of different outcomes. Results are presented with levels of confidence, often expressed as probabilities or confidence intervals, reflecting the potential variation inherent in data collection and analysis. For instance, a study might conclude with 95% confidence that the average height of a certain population falls within a specific range. This acknowledges that there’s a 5% chance the true average lies outside that range, due to sampling variability or other factors.
Quantifying uncertainty is crucial for informed decision-making. Understanding the potential range of outcomes, rather than assuming absolute precision, allows for more realistic interpretations and avoids overconfidence in findings. This practice evolved alongside the development of statistical theory and methods, driven by the need to account for variability and error in scientific inquiry. It allows researchers to transparently communicate the limitations of their studies and encourages critical evaluation of results.
