1. Don’t be wordy; mind your style! “The results that were taken from a sample of people go to show some results that tell us some facts about how women and men feel towards making friends with the opposite sex in comparison to the same sex.”

2. Cite your p-values! “By computing the p-value (value that determines whether or not null hypothesis can be rejected) with the use of the z- statistic we can see that the p-value is small enough that the null hypothesis can be rejected. The p-value in both tests (men and women) allow us to reject the null hypothesis in favor of the alternative hypothesis that both men and women find it easier to make friends with the opposite sex more so than with the same sex.” What were the p-values?

3. Understand what a p-value means (but don’t write about it) “…we calculated that in the male testing, there was a 7.8% chance that we would observe less males who find it easier to make friends with other males and a 2.0% chance that we would observe less females who find it easier to make friends with other females.” A p-value is the probability of an experiment producing results at least as extreme as those observed, assuming H 0 is true. However, it’s rarely necessary to write this in a report; simply report the p-value and state your conclusions in plain language.

4. Be precise about what your test tells you “In conclusion, the probability of females and males to choose the opposite sex as friends is about the same.” Even if we reject H 0 for both males and females, this fact says nothing about the comparison between males and females implied by this quotation.