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Instantiating sub-class from super

On 16/10/2019 19:52, 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

Yes, the problem of standing on the shoulders of others (not necessarily
giants). I assume the problematic code was tested on an OS that happened
to sort the results as required. Years ago I had a similar issue with a
race condition, but we caught it because I developed the code under
Linux and everybody else on the project ran it under Windows (where the
bug surfaced). Note to self: before I do any more parallel programming
check out how to test for race conditions.