<josef.p...@gmail.com> wrote: > Note. The editors of Basic and Applied Social Psychology are also > banning confidence intervals.
I know. I am not sure I agree on that. I don't particularly like confidence intervals very much, but I don't hate them with a passion. Pro: Even though confidence intervals have a bizarre interpretation, they are probably better than nothing. Con: Confidence intervals belongs to the reals of inferential statistics and as such they can be abused in a number of ways. One obvious case is a cheap replacement for p-values. If the purpose is to avoid inferential statistics completely, they should obviously not be allowed. Sturla > > >> >> A null hypothesis test is also just a matter of model selection: In the >> case of the classical t-test, the null hypothesis is a model selection >> between one model with a single parameter x ~ N(sigma,0) and the >> alternative hypothesis is a model with two parameters, x ~ N(sigma,mu). If >> the mean is actually 0, adding an additional parameter mu should overfit >> the data. You can e.g. see this on the BIC value. >> >> >>> and the editors of one journal agree with this >>> >>> https://groups.google.com/d/msg/pystatsmodels/e8aTj2ydyFI/odkShG2K3wwJ >>> http://www.scientificamerican.com/article/scientists-perturbed-by-loss-of-stat-tool-to-sift-research-fudge-from-fact/ >> >> Epidemiology also has a ban on p-values for more than 10 years, due to its >> founding editor. The ban was lifted when they changed editor 2001, but the >> quality of the publications dropped when p-values were reintroduced. >> >> http://journals.lww.com/epidem/fulltext/2001/05000/the_value_of_p.2.aspx > > > " > Does all this mean a change in Epidemiology’s policy on P-values? It > may be no more than a change in perception. We will not ban P-values. > But neither did Rothman. He called for caution, and we do the same. > The question is not whether the P-value is intrinsically bad, but > whether it too easily substitutes for the thoughtful integration of > evidence and reasoning. Given the P-value’s blighted history, > researchers who would employ the P-value take on a particularly heavy > burden to do so wisely. > " > I have no disagreement with that. > p-values are only one of our five columns in the results parameter table. > > I refrain from any other comments that might overlap quite a bit with > previous discussions that we had. > > Josef > >> >> The editors of Journal of Physiology have (beginning from last year) >> started to request confidence intervals instead of p-values. I know this >> because collegues in Oslo have gotten papers returned and been instructed >> to change all their analysis away from using p-values. This was not in the >> journal's instructions to authors, so it came as a surprise. >> >> I agree with the editors of Basic and Applied Social Psychology on their >> ban on p-values and classical hypothesis testing. Inferential statistics is >> seldom used correctly. Most scientists do not have the competence to know >> when to use descriptive statistics and when to use inferential statistics, >> it seems. The common practice is to always use inferential statistics, even >> when inappropriate. Thus we see papers littered with p-values. It is for >> the common good to just ban inferential statistics all together. Instead >> the editors of BASP request descriptive statistics and good graphs. The >> inference can then be done qualitatively. If an effect is not visible by >> eye balling, then it is likely not there (or at least not important). The >> scale and resolution used on a graph should reflect the relevant effect >> sizes. If the scale makes a tiny effect invisible on a graph, then it is >> not relevant even if present. This is not a new and unproven method to >> science, Isaac Newton and Albert Einstein did this too. Descriptive >> statistics combined with qualitative inference is an old and proven method >> that everyone can use correctly. Of course it would be better if scientists >> actually had the competence to use inferential statistics correctly. >> Unfortunately everything suggests that few scientists do, at least outside >> the fields of statistics and machine learning. >> >> >>> Fortunately for statsmodels, there is a large part of the world that >>> also want to know about which variables affect a event or >>> characteristic, instead of just doing best prediction with anonymous >>> variables >> >> Model selection can be blind or driven by domain-specific knowledge. In the >> latter case, we are better off using Bayesian statistics, because when >> using knowledge of a subject as guide we are including prior information in >> our analysis. Then it is better to be specific about that. >> >> >> Sturla >> >> >> ------------------------------------------------------------------------------ >> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT >> Develop your own process in accordance with the BPMN 2 standard >> Learn Process modeling best practices with Bonita BPM through live exercises >> http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual- event?utm_ >> source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT > Develop your own process in accordance with the BPMN 2 standard > Learn Process modeling best practices with Bonita BPM through live exercises > http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual- event?utm_ > source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT Develop your own process in accordance with the BPMN 2 standard Learn Process modeling best practices with Bonita BPM through live exercises http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual- event?utm_ source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
