AUDITING HAPHAZARDNESS

Inspired somewhat randomly by a paper published by the American Accounting Association, part of this project intends to develop a Haphazard Index, which will attempt to audit the positive or negative impact Haphazardness has on person or organisation.


Haphazard sampling is a non-statistical technique used by auditors to simulate random sampling when testing the error status of accounting populations.

Hall, et al (2013)

Initially it was a bit of a joke idea before slowly forming into an interesting line of inquiry, which may not be as Random (see below) as first appears.

Can similar techniques that test for errors in ‘accounting populations’ be used to measure Haphazardness? If so, is there application for such an index, particularly within computer science where it can be argued that the internet diffused in a somewhat Haphazard way?

Chance and Randomness

The quote below is from Stanford University paper, ‘Chance versus Randomness’, discussing “uses of ‘random’ to characterise an entire collection of outcomes of a given repeated process.”  If ‘repeated process’ was to be replaced by the ‘Daily Routine of a person or company’, would we be able to begin discerning the value of Haphazardness?         

Chance vs. Randomness is a another vexed issue, no doubt keeping many a philosopher awake at night, which also has implications for understanding Haphazard. We may then also possibly need, by implication, to consider Intention (Purpose) vs. Accidental action.


“The introduction of product randomness helps us make sense of some familiar uses of ‘random’ to characterise an entire collection of outcomes of a given repeated process. This is the sense in which a random sample is random: it is an unbiased representation of the population from which it is drawn—and that is a property of the entire sample, not each individual member. If a random sample is to do its job, it should be irregular and haphazard with respect to the population variables of interest. We should not be able to predict the membership of the sample to any degree of reliability by making use of some other feature of individuals in the population. (So we should not be able to guess at the likely membership of a random sample by using some feature like ‘is over 180cm tall’.) A random sample is one that is representative in the sense of being typical of the underlying population from which it is drawn, which means in turn that—in the ideal case—it will exhibit no order or pattern that is not exemplified in that underlying population.”

Eagle (2010)

References

Thomas W. Hall, Andrew W. Higson, Bethane Jo Pierce, Kenneth H. Price, and Christopher J. Skousen, “Haphazard Sampling: Selection Biases and the Estimation Consequences of These Biases”, American Accounting Association, Current Issues in Auditing, Volume 7, Issue 2 2013, Pages P16–P22, DOI: 10.2308/ciia-50568, URL =
>https://aaajournals.org/doi/pdf/10.2308/ciia-50568

Eagle, Antony, “Chance versus Randomness”, The Stanford Encyclopedia of Philosophy (Spring 2019 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/spr2019/entries/chance-randomness/>.


John M

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