**Thinking ahead, before you have done the experiment:** II some "Nice" packets ought to have shorter round-trip times and other "Ugly" packets ought to take much longer, then there is a big advantage in doing a paired test. So design the experiment to keep track of pairs. 

**If you have already done the experiment and happened to keep track of pairs:** You might see if Protocol A scores are correlated with their respective Protocol B scores. If there is significant correlation, the advantage of doing a paired test may be considerable.

**If you have data with no tracking of A/B pairs:** Then you'll have to do a 2-sample test.
Your chances of finding a significant difference is lower in this case.

**Example:** Consider vectors `x1` (Protocol A) and `x2` (Protocol B) of normal data, each with $n = 100# observations, and _with pairing._ They have the following sample summaries:

    summary(x1); sd(x1)
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      33.58   43.73   47.31   48.90   53.30   67.84 
    [1] 7.030837   # StDev

    summary(x2); sd(x2)
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      34.53   44.73   48.27   49.91   54.36   68.82 
    [1] 7.028975   # StDev  

    cor(x1, x2) 
    [1] 0.9998922  # Sample correlation

Then a paired t test has P-value `2.2e-16` (essentially 0), so there is a very clear difference between Protocols A and B (B has slightly, but significantly, longer times.)

However, if paring is lost (order of observations within vectors is scrambled), then a paired test
is not possible. A Welch 2-sample t test has
P-value `0.3137`, which provides no hint of 
significance.

_Note:_ Whether t tests can be used depends on having data that are nearly normal. But there are ways to do both paired tests and tests with two (independent) samples for non-normal data.