The fact that the 95% confidence intervals of two variables have some overlap doesn't mean there's a >5% chance that the expected values of the two variables are the same.
Consider two independent random variables X and Y; the chance that (a sample from X is above the 90th percentile of the true distribution of X) is 10%, but the chance that (a sample of X is above the 90th percentile of the true distribution of X AND a sample of Y is below the 10th percentile of the true distribution of Y) is 1%.
(disclaimer: with actual science the stats are a lot more complicated and you can't just assume they're independent and multiply the two, it's just a simplified example to give intuition about why overlapping confidence intervals don't imply what the parent thought, IANAstatistician)
Consider two independent random variables X and Y; the chance that (a sample from X is above the 90th percentile of the true distribution of X) is 10%, but the chance that (a sample of X is above the 90th percentile of the true distribution of X AND a sample of Y is below the 10th percentile of the true distribution of Y) is 1%.
(disclaimer: with actual science the stats are a lot more complicated and you can't just assume they're independent and multiply the two, it's just a simplified example to give intuition about why overlapping confidence intervals don't imply what the parent thought, IANAstatistician)