Decide on assumptions for your data. For example, when applicable, you can assume that the

Review all of the variables carefully.
Decide on assumptions for your data. For example, when applicable, you can assume that the data is normally distributed and you can use parametric methods for analysis.
You may choose to do a one or two-sample test. For instance, you can combine all of the data and run as a one-sample test, or you can compare two samples (e.g. men versus women; younger versus older; those with diabetes versus those without diabetes, etc.). If doing a one sample test, look through recent articles on the Internet for someone else's claim about the population parameter that you will support or refute.
Formulate your informal hypothesis (i.e. what you think is true). For example: "Women get fewer hours of sleep on average than men." This is a two-sample hypothesis. Or "Individuals in this study get fewer hours of sleep on average than the general population." This would be a one-sample hypothesis. State why you think your hypothesis is true. Write your formal null and alternative hypothesis.
Create a frequency distribution table for your sample(s) in Excel. Create a histogram or bar chart, as applicable.
Calculate sample mean, median, mode and standard deviation or sample proportion, as applicable, for your sample(s) in Excel.
Create a confidence interval for your sample(s).
Test your hypothesis.
Formulate a conclusion based on your results. State formally and informally.