Freedom Case

Imagine that you can predict the class's scores on the Tests for Understanding in this course. In Week 1, there was a bimodal distribution. In Week 2, there was a positive skew. In Week 3, there was a normal distribution. In Week 4, there was a negative skew. Based on this concept, I found that the test scores, which were found in Weeks 1, 2, 3, and 4, are subjective to a point. The bimodal distribution model speaks to a series of observations with two peaks, representing two statistical values that occur with equal frequency and more often than any other value. Positive and Negative Skews can either lean to the left or right, with no equality at all. Normal distribution theoretically takes on a bell shape symmetrical about the mean.

The students' scores over a period of time will fluctuate, because they will try other methods that will effectively help them reach their goal; or assist them when dealing with numbers, etc. I believe that the type of distribution the instructor most want to see if the best result is that most students earn an average score would be the Bimodal Distribution. My reason behind this is because while understanding the Bimodal Distribution model, it indulges in many variables, and illicit detailed information about the student, while not leaving out information that is needed to clearly understand what the students' real score is. This is shown by various times, colors, sizes, ages, speed, etc., which are detailed.

The type of distribution students would like to see in order to be most likely to earn a good grade probably would be between the Positive and Negative Skews. Rather than being wrong in ones analysis, students would consider being in the middle of the road, rather than being either right or wrong. Coincidently, the positive and negative skews detail this. Skewness is extremely important to finance and investing. Most sets of data, including stock prices and asset returns, have either positive or negative skew rather than following the balanced normal distribution (which has a skewness of zero). By knowing which way data is skewed, one can better estimate whether a given (or future) data point will be more or less than the mean.

Many times people expect certainty, and not a subjective conclusion. An example of one having a migraine to some Physicians are subjective, because the person experiencing it can only tell you from their perspective and feeling; versus a person having a broken bone, which is not subjective, because MRI's and other tests can indicate the injury the person has. So when it comes to bimodal distribution, positive and negative skews, and normal distribution, the conclusion is based off a person's analysis and how they have come to that specific result.

References:

Heiman, G. W. (2004). Essential statistics for the behavioral