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Instantiating sub-class from super

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