All variables cannot be accounted for. Without knowing what variables are missing in statistical data, how can you trust the results of any statistical evidence. How could one analysis be more accurate than another. People love statistical truths as they create a basis for deciding what to believe (or confirming perceptions of reality) and give only little thought to the inherent flaws in their creation.
Without knowing what variables are missing in statistical data, how can you trust the results of any statistical evidence. How could one analysis be more accurate than another.
This is an example of the Fallacy of Gray. No statistical analysis can account for absolutely everything, but one analysis can be more accurate than another, by accounting for more of the important things. A statistician earns trust the same way anyone else does: by being right in cases you can verify, and by presenting evidence of good and detailed reasoning.
All variables cannot be accounted for. Without knowing what variables are missing in statistical data, how can you trust the results of any statistical evidence. How could one analysis be more accurate than another. People love statistical truths as they create a basis for deciding what to believe (or confirming perceptions of reality) and give only little thought to the inherent flaws in their creation.
This is an example of the Fallacy of Gray. No statistical analysis can account for absolutely everything, but one analysis can be more accurate than another, by accounting for more of the important things. A statistician earns trust the same way anyone else does: by being right in cases you can verify, and by presenting evidence of good and detailed reasoning.