[HN Gopher] Engineering Statistics Handbook (2012)
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Engineering Statistics Handbook (2012)
Author : gballan
Score : 171 points
Date : 2023-10-22 05:58 UTC (1 days ago)
(HTM) web link (www.itl.nist.gov)
(TXT) w3m dump (www.itl.nist.gov)
| sieste wrote:
| Books like these should be read by all mathematics/statistics
| students, not to get better at mathematics, but to see how the
| concepts they study in theory are relevant in practice.
| Koshkin wrote:
| Indeed, statistics (as opposed to probability theory) is more
| like physics, in its relation to mathematics.
| GTP wrote:
| Anyone used this in the past? Do you think it's a good book?
| avs733 wrote:
| It's good. I provide it to students as a resource and bring it
| up on a couple of topics when I teach undergraduate engineers
| statistics
| happy3483t583 wrote:
| It's good but it's old-school, focusing on traditional null
| hypothesis significance testing and related ideas. These ideas
| have been under attack for decades, most recently as being one
| cause of the scientific replicability problem.
|
| If you're new to applied stats, be aware that there's a whole
| other world out there, beautifully taught to beginners by
| https://xcelab.net/rm/statistical-rethinking/
| brennanpeterson wrote:
| It is old school in the sense that it is quite pragmatic. It
| is aimed at Mainstays of engineering like 'did I improve
| performance in this line' or 'ate these systems giving the
| same output's or even 'how do.i design an experiment in a
| really complex system'. And they generally work well.
|
| Just do work using appropriate tools. There are reasons for
| all sorts of tools, use the right ones.
|
| Reproducibility crisis is mostly about marginal results,
| publication bias, and humans.behavior. Bayesians can (and
| do!) The same things. It isn't the tool, it is the people.
| tgv wrote:
| It's also the tool.
|
| There are whole books dedicated to it (I can recommend
| Bernouilli's Fallacy by Clayton), but the gist is: NHST
| answers the wrong question (it should answer: what is the
| likelihood of the hypothesis given the data, but answers:
| what is the likelihood of the data under the hypothesis),
| and is (thus) very sensitive to the probability of the
| prior. To quote the old example: when you wake up with a
| headache, it's not usual to assume you've got a brain
| tumor.
| happy3483t583 wrote:
| It's both.
|
| I've consulted in many situations where people make
| horrible business decisions based on "statistically
| significant." People mistake p-values for
| p(hypothesis|data), mistake p-values for effect size ("it's
| _highly_ significant! "); moreover, people using NHST don't
| understand multiplicity or "topping-off" problems.
|
| Of course one could argue that they just need to review
| Intro Stats, but that skips over the impenetrable
| conceptual nature of p-values and NHST. Given that _regular
| people must use applied stats_ and given that NHST is
| fundamentally arcane and confusing, then the stage is set
| for endless drama.
|
| Now if we were sampling manufactured products from batches
| and looking for a quantitative upper-bound on the failure
| rates, run over many batches, then we have a winner!
| crispyambulance wrote:
| It seems that the problems you cited are still very much
| people-related.
|
| There's a lot of pressure (especially in regulated
| environments) to be "data-driven". It's tempting for
| folks to pick up a tool like minitab and churn some
| stats, cargo-cult-style, to prove whatever and look-smart
| while doing it.
|
| This is tricky stuff. Nobody like to admit they're
| confused or that the path isn't clear. I agree it sets
| the stage for drama and failure.
| mdp2021 wrote:
| Excellent, Richard McElreath also publishes lectures full
| lectures at
|
| https://www.youtube.com/@rmcelreath/videos
| stiff wrote:
| The book has eight voluminous chapters and only one is about
| hypothesis testing. Much of it is about design of experiments
| and statistical process control, think something like
| optimizing the workings of a factory. Hypothesis testing has
| been under attack in psychology/economics/etc., as part of I
| think a broader problem those disciplines have drawing
| reliable conclusions in general, since it is difficult to
| control all the variables. This book is about engineering and
| industrial applications which are closer to physics.
| hcks wrote:
| Any other recommendation with the same focus?
| iancmceachern wrote:
| There is a book called "statistics for engineers and scientists
| "
| ta_tunestub wrote:
| "The Book of Why" and "Statistical Rethinking" course [1]
|
| [1]https://twitter.com/chrismgreer/status/1714687870286655885?s
| ...
| dpflan wrote:
| Is there an updated version?
| crispyambulance wrote:
| It's maintained, minor changes as late as January 2023
| (https://www.itl.nist.gov/div898/handbook/changes.htm).
|
| This is stable stuff!
|
| The retro html needs a bit of a refresh, but everything seems
| to work AFAIK.
| boredemployee wrote:
| I read a few mins of the book and loved it, but I confess that
| the comments here have left me a bit confused.
|
| I have a degree in engineering, took two statistics courses that
| basically used high school level math.
|
| Nowadays, I work with data analysis, using SQL and Python. And I
| would like to know which statistical approach you guys think are
| most suitable for the real world, like how to test hypothesis
| etc?
| brutusborn wrote:
| I think the examples in the handbook are excellent, as an
| engineer I started on the pipeline example [1]. In terms of
| testing hypothesis, the design of experiments section is useful
| [2].
|
| [1]
| https://www.itl.nist.gov/div898/handbook/pmd/section6/pmd62....
| [2] https://www.itl.nist.gov/div898/handbook/pmd/section3/pmd31
| .....
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