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1500 questions
57
votes
8 answers
Why continue to teach and use hypothesis testing (when confidence intervals are available)?
Why continue to teach and use hypothesis testing (with all its difficult concepts and which are among the most statistical sins) for problems where there is an interval estimator (confidence, bootstrap, credibility or whatever)? What is the best…
Washington S. Silva
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57
votes
3 answers
ANOVA assumption normality/normal distribution of residuals
The Wikipedia page on ANOVA lists three assumptions, namely:
Independence of cases – this is an assumption of the model that simplifies the statistical analysis.
Normality – the distributions of the residuals are normal.
Equality (or "homogeneity")…
Roman Luštrik
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57
votes
3 answers
What is the difference between a Normal and a Gaussian Distribution
Is there a deep difference between a Normal and a Gaussian distribution, I've seen many papers using them without distinction, and I usually also refer to them as the same thing.
However, my PI recently told me that a normal is the specific case of…
Leon palafox
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57
votes
4 answers
Manually Calculating P value from t-value in t-test
I have a sample dataset with 31 values. I ran a two-tailed t-test using R to test if the true mean is equal to 10:
t.test(x=data, mu=10, conf.level=0.95)
Output:
t = 11.244, df = 30, p-value = 2.786e-12
alternative hypothesis: true mean is not…
herbps10
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57
votes
7 answers
Interview question: If correlation doesn't imply causation, how do you detect causation?
I got this question:
If correlation doesn't imply causation, how do you detect causation?
in an interview.
My answer was: You do some form of A/B testing. The interviewer kept prodding me for another approach but I couldn't think of any, and he…
Akaike's Children
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57
votes
3 answers
Box-Cox like transformation for independent variables?
Is there a Box-Cox like transformation for independent variables? That is, a transformation that optimizes the $x$ variable so that the y~f(x) will make a more reasonable fit for a linear model?
If so, is there a function to perform this with R?
Tal Galili
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57
votes
1 answer
Why do we use Kullback-Leibler divergence rather than cross entropy in the t-SNE objective function?
In my mind, KL divergence from sample distribution to true distribution is simply the difference between cross entropy and entropy.
Why do we use cross entropy to be the cost function in many machine learning models, but use Kullback-Leibler…
JimSpark
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57
votes
11 answers
How to decide on the correct number of clusters?
We find the cluster centers and assign points to k different cluster bins in k-means clustering which is a very well known algorithm and is found almost in every machine learning package on the net. But the missing and most important part in my…
petrichor
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57
votes
10 answers
Who are frequentists?
We already had a thread asking who are Bayesians and one asking if frequentists are Bayesians, but there was no thread asking directly who are frequentists? This is a question that was asked by @whuber as a comment to this thread and it begs to be…
Tim
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57
votes
6 answers
Alternatives to logistic regression in R
I would like as many algorithms that perform the same task as logistic regression. That is algorithms/models that can give a prediction to a binary response (Y) with some explanatory variable (X).
I would be glad if after you name the algorithm,…
Tal Galili
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57
votes
10 answers
What are some examples of anachronistic practices in statistics?
I am referring to practices that still maintain their presence, even though the problems (usually computational) they were designed to cope with have been mostly solved.
For example, Yates' continuity correction was invented to approximate Fisher's…
Francis
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57
votes
9 answers
Reference book for linear algebra applied to statistics?
I have been working in R for a bit and have been faced with things like PCA, SVD, QR decompositions and many such linear algebra results (when inspecting estimating weighted regressions and such) so I wanted to know if anyone has a recommendation on…
Palace Chan
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57
votes
1 answer
Should I normalize word2vec's word vectors before using them?
After training word vectors with word2vec, is it better to normalize them before using them for some downstream applications? I.e what are the pros/cons of normalizing them?
Franck Dernoncourt
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57
votes
1 answer
How to apply standardization/normalization to train- and testset if prediction is the goal?
Do I transform all my data or folds (if CV is applied) at the same time? e.g.
(allData - mean(allData)) / sd(allData)
Do I transform trainset and testset separately? e.g.
(trainData - mean(trainData)) / sd(trainData)
(testData - mean(testData)) /…
DerTom
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57
votes
3 answers
Won't highly-correlated variables in random forest distort accuracy and feature-selection?
In my understanding, highly correlated variables won't cause multi-collinearity issues in random forest model (Please correct me if I'm wrong). However, on the other way, if I have too many variables containing similar information, will the model…
Yoki
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