State the hypothesis for the data analysis. Normally, you would state a null hypothesis that the data's mean is equal to zero. For ratios, however, you should state that the data's mean is equal to one, because this would imply no differences between the two sides of the ratio. (For example, there is no difference between the number of males and females in math courses.)
Determine the alpha value of your study. This is the value that indicates the probability of a false negative in your results. The standard alpha value in most scientific fields is 0.05.
Find the mean of the data set. Sum the ratios and divide by the number of data points.
Find the variance of the data set. Subtract the mean from each of the data points. Square the resulting numbers. Sum the resulting squares. Divide this sum by one less the number of ratios in your data set. What you are left with is the variance.
Calculate the t-statistic. Subtract one from the mean of the data set and divide this number by the square root of the variance. Multiply the result by the square root of the number of data points in the data set. The resulting number is the t-statistic.
Compute the degrees of freedom for the study. The degrees of freedom is simply the number of data points in the study minus one.
Use a t-table, which can be found in any introductory statistics text, to find the p-value for your data. Use the degrees of freedom and the t-statistic you calculated to find the row and column of the p-value.
Compare the p-value to the alpha value you chose. If the p-value is smaller, your data implies that there is a difference between the two groups in the ratio. This result means that your data has an interesting result and can be interpreted in terms of the ratio. Otherwise, your data is uninteresting and there is no implication present other than there being a lack of evidence that the two groups in the ratio differ.