Weight Loss Case Study

As far as did all the people using the machine lose 10 pounds, I particularly don 't believe that all did and it 's very doubtful that all the participants accomplished this goal. Many individuals lose weight in different methods and at different times. In order for me to believe that all participants using the exercise machine resulted in an average 10-pound weight loss, I would need to see additional information relating to all participants previous weight and the average pounds lost per each participant. Sometimes seeing is believing. In most cases we would need to witness this ourselves and make sure other factors are not tied into the weight loss mechanics. In my opinion, there are a lot of essential elements and information lacking

1549) implies that, “Hypothesis tests can be a useful statistical tool for model validation. In this sense, model validation is a process of adding strength to our belief in the predictiveness of a model by repeatedly showing that it is not blatantly wrong in specific applications.” To help prove the validity of the results in this case study, a hypothesis test would definitely need to be

As far as the average of weight loss, that could factor in that some participants actually lost more than 10 pounds and some lost less, but on average the users lost approximately 10 pounds. Furthermore, some could have lost a lot more than anticipated and therefore cause an inaccuracy in the average. Perhaps knowing how much each person lost actually would present more information. I would also want to know if the participants were incorporating more weight loss options such as dieting or diet pills and in addition how long they were on the machine. Based on Siegfried (2010, p. 29) implies that,“When testing hypotheses, “Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or is merely a random fluke, but the standard methods mix mutually inconsistent philosophies and offer no meaningful basis for making such decisions. Even when performed correctly, statistical tests are widely misunderstood and frequently