by Rejecting positivism — the case of statistics Rejecting positivism requires re-thinking the disciplines related to data analysis from the foundations. In this paper, we consider just one of the foundational concepts of statistics. The question we will explore is: What is the relationship between the numbers we use (the data) and external reality? The standard conception promoted in statistics is that numbers are FACTS. These are objective measures of external reality, which are the same for all observers. About these numbers there can be no dispute, as all people who go out and measure would come up with the same number. In particular, there is no element of subjectivity, and there are no value judgments, which are built into the numbers we use. Our main
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Lars Pålsson Syll considers the following as important: Statistics & Econometrics
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Rejecting positivism — the case of statistics
Rejecting positivism requires re-thinking the disciplines related to data analysis from the foundations. In this paper, we consider just one of the foundational concepts of statistics. The question we will explore is: What is the relationship between the numbers we use (the data) and external reality? The standard conception promoted in statistics is that numbers are FACTS. These are objective measures of external reality, which are the same for all observers. About these numbers there can be no dispute, as all people who go out and measure would come up with the same number. In particular, there is no element of subjectivity, and there are no value judgments, which are built into the numbers we use. Our main goal in this paper is to show that this is not true. Most of the numbers we use in statistical analysis are based on hidden value judgements as well as subjective decisions about relative important of different factors. It would be better to express these judgments openly, so that there could be discussion and debate. However, the positivist philosophy prohibits the use of values so current statistical methodology HIDES these subjective elements. As a result, students of statistics get the impression that statistical methods are entirely objective and data-based. We will show that this is not true, and explain how to uncover value judgments built into apparently objective forms of data analysis.
If anything, Zaman’s paper underlines how important it is not to equate science with statistical calculation. All science entail human judgement, and using statistical models doesn’t relieve us of that necessity. Working with misspecified models, the scientific value of statistics is actually zero — even though you’re making valid statistical inferences! Statistical models are no substitutes for doing real science.
We should never forget that the underlying parameters we use when performing statistical tests are model constructions. And if the model is wrong, the value of our calculations is nil.
All of this, of course, does also apply when we use statistics in economics. Most work in econometrics and regression analysis is — still — made on the assumption that the researcher has a theoretical model that is ‘true.’ Based on this belief of having a correct specification for an econometric model or running a regression, one proceeds as if the only problem remaining to solve have to do with measurement and observation.
When things sound too good to be true, they usually aren’t. And that goes for econometrics too. The snag is that there is pretty little to support the perfect specification assumption. Looking around in social science and economics we don’t find a single regression or econometric model that lives up to the standards set by the ‘true’ theoretical model — and there is pretty little that gives us reason to believe things will be different in the future.