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Instantiating sub-class from super

On 18/10/2019 23:57, DL Neil wrote:
> On 17/10/19 7:52 AM, MRAB wrote:
>> On 2019-10-16 19:43, duncan smith wrote:
>>> On 16/10/2019 04:41, DL Neil wrote:
>>>> On 16/10/19 1:55 PM, duncan smith wrote:
>>>>> On 15/10/2019 21:36, DL Neil wrote:
>>>>>> On 16/10/19 12:38 AM, Rhodri James wrote:
>>>>>>> On 14/10/2019 21:55, DL Neil via Python-list wrote:
>>>>>> ...
>>>>>> So, yes, the "label" is unimportant - except to politicians and
>>>>>> statisticians, who want precise answers from vague collections of
>>>>>> data... (sigh!)
>>>>> [snip]
>>>>> No not (real) statisticians. People often want us to provide precise
>>>>> answers, but they don't often get them.
>>>>> "It ain?t what you don?t know that gets you into trouble. It?s what
>>>>> you
>>>>> know for sure that just ain?t so." (Mark Twain - perhaps)
>>>> +1
>>>> Although, you've undoubtedly heard people attempt to make claims of
>>>> having 'accurate figures' (even, "that came from Stats") when you told
>>>> them that the limitations and variations rendered the exercise
>>>> laughable...
>>>> My favorite (of the moment) is a local computer store who regularly
>>>> offer such gems as: (underneath the sales (web-) page for an upmarket
>>>> *desktop* computer)? "people who bought this also bought" followed
>>>> by at
>>>> least two portable PC carry cases. They must be rather large
>>>> carry-bags!
>>>> (along with such surprises as keyboard, mouse, ...)
>>>> This morning I turned-down a study for a political group. One study has
>>>> already been completed and presented. The antagonist wanted an A/B
>>>> comparison (backing his 'side', of course). I mildly suggested that I
>>>> would do it, if he'd also pay me to do an A/B/C study, where 'C' was a
>>>> costing - the economic opportunity cost of 'the people' waiting for
>>>> 'the
>>>> government' to make a decision - (and delaying that decision by waiting
>>>> for "study" after "study" - The UK and their (MPs') inability to decide
>>>> "Brexit" a particularly disastrous illustration of such)
>>>> Sorry, don't want to incur the anger of the list-gods - such
>>>> calculations would be performed in Python (of course)
>>> Clearly, all such analyses should be done in Python. Thank God for rpy2,
>>> otherwise I'd have to write R code. It's bad enough having to read it
>>> occasionally to figure out what's going on under the hood (I like
>>> everything about R - except the syntax).
>>> ?> I have too many examples of people ignoring random variation, testing
>>> hypotheses on the data that generated the hypotheses, shifting the
>>> goalposts, using cum / post hoc ergo propter hoc reasoning, assuming
>>> monocausality etc. In some areas these things have become almost
>>> standard practice (and they don't really hinder publication as long as
>>> they are even moderately well hidden). Of course, it's often about
>>> policy promotion, and the economic analyses can be just as bad (e.g.
>>> comparing the negative impacts of a policy on the individual with the
>>> positive impacts aggregated over a very large population). And if it's
>>> about policy promotion a press release is inevitable. So we just need to
>>> survey the news media for specific examples. Unfortunately there's no
>>> reliable service for telling us what's crap and what isn't. (Go on,
>>> somebody pay me, all my data processing / re-analysis will be in Python
>>> ;-).)
>> Even when using Python, you have to be careful:
>> Researchers find bug in Python script may have affected hundreds of
>> studies
> I think both of our 'Python' comments were made tongue-in-cheek. Sadly
> the tool won't guarantee the result...
> At my first research project, before I'd even completed my first degree,
> I noticed a similar fault in some code*. There was I, the youngest,
> newest, least-est member of staff, telling the prof/boss and all the
> other researchers that they'd made a serious error, upon which various
> papers had been based plus a white-paper for government consideration.
> Oops!
> (Basic-Plus on DEC PDP/Vax-en introduced a 'virtual storage array', ie
> on-disk cf in-RAM. However, it did not wipe the disk-space prior to use
> (whereas arrays were zero-ed, IIRC). Thus, random data purporting to be
> valid data-entered. Once corrected and re-run "my results" (as they were
> termed - not sure if insult or compliment) were not hugely different
> from the originals).
> All we can do, is add some checks-and-balances rather than relying on
> 'the computer'.
> Upon which point: those of us who learned 'complicated math' with the
> aid of a slide-rule, employ a technique of mentally estimating the
> result in both the first first few digits and scale - and thus noticing
> any completely incongruous 'result'. Even with lessons in "The
> Scientific Approach" am not aware that the 'calculator' or 'computer
> generations' were/are taught such 'common sense'...

I always remember the Hubble mirror fiasco, where the problem could have
been detected using a tape measure. Also, from my days in the building
industry "measure twice, cut once". As far as claims in (social)
scientific publications are concerned I always tell e.g. PhD students to
assume nothing and check everything. I only recently discovered that
this is pretty much the same as the ABC of policing, "assume nothing,
believe no-one, challenge everything".