Statistical perfection is over the horizon
What a fascinating topic!
"Statistical perfection" is a concept that has been debated among statisticians, data scientists, and researchers for a long time. It's a notion that suggests that it's possible to achieve a state where our statistical models, methods, and analyses are so accurate and reliable that they can be considered "perfect."
However, as you've aptly phrased it, "statistical perfection is over the horizon." In other words, it's a goal that may be elusive, if not impossible, to achieve. Here are a few reasons why:
- Complexity of real-world data: Real-world data is often messy, noisy, and complex. It's difficult to account for all the variables, interactions, and non-linear relationships that exist in the data. As a result, even the most sophisticated statistical models may not be able to capture the underlying patterns and relationships perfectly.
- Limitations of statistical methods: Statistical methods are based on mathematical assumptions and simplifications. These assumptions may not always hold true in practice, leading to biases, errors, and limitations in the results. Additionally, new data and new phenomena can emerge, which may not be accounted for by existing statistical methods.
- Uncertainty and randomness: Statistical analysis is inherently uncertain and subject to random fluctuations. Even with large sample sizes and advanced methods, there will always be some degree of uncertainty and variability in the results.
- Human judgment and bias: Statistical analysis is not a purely objective process. Human judgment and bias can influence the choice of methods, the interpretation of results, and the conclusions drawn from the data.
- The need for ongoing refinement: Statistical methods and models are not static; they need to be continually refined and updated to reflect new knowledge, new data, and new challenges.
In light of these challenges, it's clear that "statistical perfection" is indeed over the horizon. Instead, we should strive for continuous improvement, refinement, and innovation in statistical methods and practices. By acknowledging the limitations and uncertainties of statistical analysis, we can work towards developing more robust, reliable, and effective methods for extracting insights from data.