Nope . Not a fan . But I know a good opportunity when I see one <| sep |> Wow that ' s rude ! Not only to you , but the customer you were helping as well . After 7 years in customer service , I shouldn ' t be surprised , but they just keep getting worse <| sep |> Thanks ! I noticed it looks slightly better today , and it has been cold so that could definitely have been a factor what with the runny nose and all . <| sep |> AND his jocks ? Not hep ' nin mate <| sep |> At the moment , I am assuming that everything that happens everywhere is satire . I think that ' s just the safest option . President Trump ! hahahah , gosh they got me on that one . <| sep |> It seems to fail on git . Try installing it with sudo aptget install git <| sep |> Edit I just reread my comment and I fear it can be taken as rude or condescending , that is not at all the tone I wanted to give this comment . I meant to sound inviting to see how much stuff you can get done . Given how I have an incredibly though Statistics final next week , this question made me giggle a bitThere are many , many tricky parts of statistics . Sampling by itself deserves years and years of studying , and getting a good sample for what you want to do is borderline an art . Then there is the mathematical formalisation of what you ' re doing . You can work any problem into a Decision Theory framework , where you have to determine a parameter space which is the set where the parameter you want lives . a sample space which is the set of all the possible values a sampling can take . Here enters all the probability theory available to mankind , since a sample is a realisation of a random variable . a ' loss ' function that quantifies your error the decision rule , which is a random function that takes values from that sample space and gives you the approximation that you want . From this , you can see this involves some heavy lifting in mathematics , and every step can get as tricky as the next one . Then there is the whole issue of quantifying the performance of your decision ie with what probability would your statistic correctly portrays the parameter you want . There are thousands of super cool and complex applications of statistics , ranging from quantum mechanics to number theory to geology , where you not only have to understand all the mathematical constructs a statistical analysis requires , but also you have to understand how you can work your given problem into a good , reliable and revisable framework . As an analogy , this is like asking what ' s the challenging part of algebra if the only thing you need to do in algebra is plugging in the values of a system of equations . Statistics is a vast and beautiful discipline , and a good understanding of statistical inference will be enormously useful in any other discipline .