Recently, a large music streaming service has taken steps to try to rid its "new music" recommendation algorithm of gender bias. Specifically, when suggesting a group of new artists to its users, the company's goal is for the gender distribution of the recommended artists to match the gender distribution of all artists on the streaming platform. Listed below are the proportions of all artists on the streaming service from each gender group (that the company collects information about): Gender Non-Binary Female Male Proportion 0.08 0.43 0.49 The company collects a random sample of 120 recommended artists from its new algorithm. Here is the gender information for the 120 sampled recommended artists: Gender Non-Binary Female Male Observed Counts 4 48 68 Perform a chi-square test for goodness of fit to determine if the algorithm is truly making recommendations proportional to the total number of artists of each gender category on the streaming platform. Make sure to include the hypotheses, expected counts, checked conditions, degrees of freedom, chi-square test statistic, P-value, and conclusion. You may use the DCMP Chi-Square Test tool at https://dcmathpathways.shinyapps.io/ChiSquaredTest/ to answer the question. Use a significance level of = 0.05