Statistical significance of difference in estimates

Can it be possible that confidence intervals of two estimates intersect but the difference between estimates is statistically significant?

I can see the opposite result when CIs of two estimates do not intersect, yet the difference is not statistically significant. Consider CI(b2) includes 0 but does not include b1. As the difference usually has wider CI than b2, CI(b1 - b2) therefore includes 0.

Can anybody give an example when the difference is statistically significant indeed? Simulated calculations would be a nice illustration to the proof.

This was straightforward to produce an example of.

Here's a plot of two data sets with 95% CI for the population mean for each, showing substantial overlap:

But the ordinary two sample t-test of the difference in means is significant at the 5% level (p=0.0487).

The samples:

x <- c(-0.298962792076644, 1.31710182405988, -0.0766589533299794,
0.0337168103544878, 0.613459873853599, -0.15890274057914, -0.390659755933809,
1.53050298362584, 0.0499823542564353, 0.466616365382981, 0.744105279446008,
0.347365715075919, -0.390328944660933, -1.51499397031234, 0.307921692331803)

y <- c(2.24809443786109, 1.19177585960823, 0.199827407525814, 0.250899031428787,
1.23762215395455, -0.154050289186218, 0.457502634532336, -0.783354267205565,
1.3261971790762, 1.00561325604448, 1.40114939526361, 2.15511419519248,
-0.211169335193589, 1.17701452262601, 0.220094909794722)


The two-sample test:

t.test(x,y,var.equal=TRUE)

Two Sample t-test

data:  x and y
t = -2.0606, df = 28, p-value = 0.04874
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.215344491 -0.003597556
sample estimates:
mean of x mean of y
0.1720177 0.7814887


The CI's for each sample mean (some lines omitted):

t.test(x,confint=TRUE)

95 percent confidence interval:
-0.2392346  0.5832701
mean of x
0.1720177

t.test(y,confint=TRUE)

95 percent confidence interval:
0.298467 1.264510
mean of x
0.7814887