Significance loss after removing one point from a small sample

Question

I have a consistent data about a decision making problem where I need to decide whether I choose A or B to compete, the winner is the one with larger number per round. If I remove one point, A beats B each time. But the sample is so small that removing a data point makes the data much smaller.

The goal of this competion is to maximize the result with low standard deviation. So I have here a non-dominating situation because player B has the largest record 86 while A has the smallest SD (about 150% while B 250%).

If I make a conclusion based on smaller sample, I feel a mistake -- how can I say this statistically? How much less significant will a conclusion be if I use smaller sample?

R stands for round.

R   A   B
1   85  86
2   83  83
3   83  82
4   83  82
5   83  81
6   81  80
7   80  78

[Update]

The player I choose have to compete in the same game again against other players. I want to maximize the future rounds. Look B has a rising trend while A has historically more stable results. Will do some analysis soon.

Answer 1

Much of what you say is not very clear...it seems that, whether you omit the first round or not, you should still prefer A to B. But I'll take a stab at an answer: don't omit any data, and choose A over B.

I would only omit that first round if it were clearly an error (and I'd prefer to fix it rather than omit it).

Perhaps you're thinking: go with the one with the maximum value? I would avoid the maximum as a statistic, as it's too much influenced by outlying values. So I'd suggest reformulating your statistic for comparing A and B.