Alternatively, we were encouraged to use the z-Test and the Chi-Squared test in conducting our statistical analysis. For this experiment, we cannot use Chi-Squared because age is not categorical and we are not testing a proportion, and we cannot use the z-Test because we do not have the population parameters. Thus, we concluded that the independent t-Test was our best fit. At first we were considering a one-tailed t-test to determine if female riders had a significantly lower average age than male riders, but eventually determined that we did not have a strong enough reason to pre-determine the direction of our significance, thus we used the two-tailed t-test. 

Conclusion

Based on the outcome of the t-Test, we were able to reject the null hypothesis and assert that there is a significant difference between the average age of female and male users. To strengthen the analysis, we could increase the number of months that we consider in our dataset. To add additional value to this analysis, we could try to identify whether there is a significant difference between the ages of users using Citi Bike recreationally versus those using Citi Bike for commuting to work, based on gender. This could help to better inform the user base and where future stations should be built.