Hypothesis testing of two dependent samples when pair information is not given For two samples equal sample size $(n\ =\ 100)$ that are randomly selected and dependent,  only the sample means$(\overline x_1, \overline x_2)$ and sample standard deviations($ s_1,s_2 $) for both samples are given and the pair data for two samples are not given.
Also, it is given that population variances are equal for the two samples.
The question is to find whether there is a difference in mean between sample 1 and sample 2.
How do I solve this problem. Since the two samples are dependent , can I use the two-tailed t-test for difference between means ?

But my problem is, since the data pairs are not given, how do you find the terms $ \overline d$ and $s_d$.
Or is there another formula for solving this type of question ?

If you only have information about each sample separately and nothing about either the pair-differences or say the pair-correlation (nor covariance, nor any other relevant information), you don't have the ability to work out $s_d$. If you assume that the dependence is non-negative (an uncontroversial assumption in most circumstances) you can bound $s_d$ by the usual independent samples pooled standard deviation estimate.
So if all you have is $\bar{x}$, $s$ and $n$ for each sample, and literally nothing more, there's likely nothing to do but treat it as if it were an independent test and work with what should then be an upper bound on the p-value.
It would be good to see the context, because sometimes there's some subtlety that may not be obvious on first glance which, naturally, we won't see in a paraphrased question.
