Data tends to have a certain amount of random variability. For example, one could observe that a particular woman purchased primarily beef while a particular man purchased mostly chicken. However, that might just be random chance so we'd sample several men and women and note what the buying preference was on average. A larger sample (more men and women) would give us a more accurate measure of this phenomenon, just as one sample is easily very inaccurate. If we sampled all of them then we'd just know the answer. However, it's not easy to sample everyone so we randomly sample a subset, a group of sufficient size that we can infer what the true values for the population are likely to be with a degree of accuracy that we're satisfied with.
The significantly different part of the statement refers to a belief that whatever differences you've observed were unlikely to have occurred by random chance so we believe that the differences between the two groups are real and representative of what men and women purchase in general.
More specifically, if the typical 0.05 cutoff were used then it is tantamount to saying that, "if there really was no difference between the groups then the probability of this data occurring is quite small, probability <0.05, and that is so small that we don't believe that there really is no difference." That's typically what saying "significantly different" really means... even though it's not what's usually intended.
PS: which is rather convoluted method at determining that the groups are different since you need to hypothesize there really is no difference in the first place... but that's the way it's usually done