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- Dictionaryinsignificant/ˌɪnsɪɡˈnɪfɪk(ə)nt/
adjective
- 1. too small or unimportant to be worth consideration: "the sum required was insignificant compared with military spending"
- 2. meaningless: "insignificant yet enchanting phrases"
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Nov 14, 2017 · Say if an A/B test proves to be insignificant but the test group shows that its doing better than the control group, what would be some of the reasons that the test is insignificant?
Sep 29, 2021 · Now say in the short regression, $\hat{\alpha_1}= 10$. and in the interaction model, say $\beta_2$ is small and statistically insignificant, but $\hat{\beta_1} \approx \hat{\alpha_1}$. and both are significant.
Mar 26, 2012 · $\begingroup$ Something about the use of the word "insignificant" rubs me the wrong way. Saying that some result is statistically insignificant makes me think that the result does not matter. However, the result does matter regardless of whether it is statistically significant or not statistically significant. $\endgroup$ –
Jan 20, 2022 · It is not true in general that an insignificant variable has no effect on the response. A variable can be insignificant because the sample size is too low or the random variation too large to find a clear significant effect even if an effect in fact exists, or because it is correlated with other variables and the data cannot know how much of the effect of the correlated variables belongs to what individual variable.
Sep 2, 2015 · In some cases, we can remove variables because they are insignificant in explaining the response. But in some cases, even insignificant variables must be kept. Probably the easiest way, but not necessarily the best, would to remove the most insignificant variable one at a time until all remaining variables are significant. Hope this helps!
May 25, 2015 · $\begingroup$ There's no need to run the model again after doing cross-validation (you just get the coefficients from the output of cv.glmnet), and in fact if you fit the new logistic regression model without penalisation then you're defeating the purpose of using lasso.
Jul 22, 2019 · Just define this "dummy" as a within-subject factor, and the model would do the rest. Significance itself is not very informative; it is required but not sufficient; any model would get significant with a sufficiently large number of observations. you may want to get effects size, like (partial) Eta-Squared, to get an idea of how "big" your effect is.
Jul 5, 2019 · IE: ok, yes, there's a trend / run in the data.. but it's so slight and insignificant that the statistics suggest it's not worth pursuing further analysis of. An insignificant trend is something that may be attributable to some kind of bias in the research.. maybe something very minor.. something that may just be a one time occurence in the experiment that happened to create a slight trend.
Jul 4, 2012 · The idea is to define an interval of insignificance called the window of equivalence. This is used a lot when trying to show that a generic drug is a suitable replacement for a marketed drug. A good source to read about this is William Blackwelder's paper titled “Proving the null hypothesis” in clinical trials .
May 27, 2015 · $\begingroup$ This seems useful. Can I please check my understanding: If an F-Test suggests the full model (aka all variables) is inadequate (as in, the f-statistic is less than the region of rejection), then that might be because two variables are significant but are highly correlated.
