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I am working on a binary classification problem taking one categorical and four numeric variables. I started with t-test and logistic regression, which resulted in high p-values for all the variables I considered.

As, all the variables I considered are statistically insignificant, what should be my next approach for the classification task?

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    $\begingroup$ If you want to classify, you are not interested in hypothesis testing, so should not be interested in $p$-values but rather in classification accuracy. $\endgroup$ – Tim Dec 8 '15 at 10:54
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This only means that there are no linear relationships between predictors and the decision; there is still a chance some more complex method would find them (obviously at a greater risk of overfitting).

You may give it a try with random forest, it finds much more complex iterations than regression and still is pretty hard to overfit.

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To summarize previous two posts:

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  • $\begingroup$ Welcome to our site! This answer would be more valuable if you could give a bit more of an overview of the material in the links you have given. We are trying to build a permanent repository of high-quality statistical information in the form of questions & answers. Thus, we're wary of link-only answers, and answers that are effectively a curated set of links, due to linkrot. $\endgroup$ – Silverfish Dec 8 '15 at 11:12
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    $\begingroup$ Ok, thanks for the information. I will hold that in mind for the future, and, if time allows, I will try and modify my answer. $\endgroup$ – Archie Dec 8 '15 at 12:09
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If your predictors are not significant, that generally means they are not useful for predicting your variable of interest.

So you need to find new predictors, preferably some that are strongly related to your variable of interest.

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