Critique Paper

Monday, December 6, 2021 3:06:08 PM

Critique Paper

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Part 1: Summary Critique

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Generally, it is an objective analysis of any piece of work not depending on its genre , which includes your personal thoughts on the subject. You need to give the reader an idea of whether the author of an article based it on facts and credible information. Your main goal is to show your personal opinion, backed with evidence and arguments, so you need to be very attentive while reading the article and noting down key elements. Many students fail to complete this task, as they simply provide a summary of the analyzed paper, forgetting about personal approach and challenging your own skills and knowledge.

That is why it is so simple to make one of many mistakes, while completing an article critique. These mistakes may include:. If you follow all the recommendations, you will easily complete an outstanding article critique without any efforts! One of the ways of simplifying the whole process of completing the assignment is using an article critique example. Many students decide to download such samples in order to get an overall idea of how the assignment should look like and what key points should be added. It is your way of making the whole process of creating an article critique simple and fun, following all the common rules and regulations.

With the help of such samples you will be able to save lots of times and nerves, which will definitely contribute to the overall results. It is not a secret that most of professors pay careful attention not only to the content of the assignment but also how well it is formatted. That is why you need to be very attentive, when shaping your work and adding final changes. One of the most popular formatting styles, while completing an article critique is American Psychological Association APA format, which has its specific rules and guidelines. Your paper should be double-spaced, using 1-inch margins and Times New Roman font in 12 point. The general structure of your critique should consist of a title page, abstract, body and references.

When formatting the title page, you should indicate name of your paper and school, as well as your personal data. In-text citations should be made using the author-date system, which means that you only need to indicate name of the author, followed by the year of publication. If you want to quote a certain part of the paper, you need to include the page name at the end. If you know how to write an article critique, you will easily complete the assignment not depending on its complexity and formatting peculiarities.

It should not look like this:. This histogram shows miles driven for the first car in the dataset. There are two important features of this distribution. Think about what that means. Between Time 1 and Time 2, just as many people drove 40, miles as drove 20, as drove 10, as drove 1, as drove miles, etc. Second, you can also see that the miles driven data abruptly end at 50, miles. There are 1, customers who drove 40,, miles, 1, customers who drove 45,, miles, and zero customers who drove more than 50, miles. This is not because the data were winsorized at or near 50, The highest value in the dataset is 49,, and it appears only once.

The drop-off near 50, miles only makes sense if cars that were driven more than 50, miles between Time 2 and Time 1 were either never in this dataset or were excluded from it, either by the company or the authors. We think this is very unlikely [ 7 ]. A more likely explanation is that miles driven was generated, at least in part, by adding a uniformly distributed random number, capped at 50, miles, to the baseline mileage of each customer and each car. This is easy to do in Excel e.

First, this uniform distribution of miles driven is not only observed for the first car, but for all four cars:. The mileages reported in this experiment were just that: reported. They are what people wrote down on a piece of paper. And when real people report large numbers by hand, they tend to round them. Of course, in this case some customers may have looked at their odometer and reported exactly what it displayed. But undoubtedly many would have ballparked it and reported a round number. In fact, as we are about to show you, in the baseline Time 1 data, there are lots of rounded values.

Let's consider what this implies. It implies that thousands of human beings who hand-reported their mileage data to the insurance company engaged in no rounding whatsoever. For example, it implies that a customer was equally likely to report an odometer reading of 17, miles as to report a reading of 17, This is not only at odds with common knowledge about how people report large numbers, but also with the Time 1 data on file at the insurance company.

These data are consistent with the hypothesis that a random number generator was used to create the Time 2 data. In the next section we will see that even the Time 1 data were tampered with. Interlude: Calibri and Cambria. Perhaps the most peculiar feature of the dataset is the fact that the baseline data for Car 1 in the posted Excel file appears in two different fonts. Specifically, half of the data in that column are printed in Calibri, and half are printed in Cambria.

The different fonts are easier to spot if you focus on the font size, because Cambria appears larger than Calibri. The analyses we have performed on these two fonts provide evidence of a rather specific form of data tampering. We believe the dataset began with the observations in Calibri font. Those were then duplicated using Cambria font. In that process, a random number from 0 to 1, e. In the next two sections, we review the evidence for this particular form of data tampering [ 10 ].

What is the evidence for that? First, the baseline mileages for Car 1 appear in Calibri font for 6, customers in the dataset and Cambria font for 6, customers in the dataset. So exactly half are in one font, and half are in the other. For the other three cars, there is an odd number of observations, such that the split between Cambria and Calibri is off by exactly one e. Second, each observation in Calibri tends to match an observation in Cambria. To understand what we mean by "match" take a look at these two customers:. For all four cars, these two customers have extremely similar baseline mileages.

Before the experiment, these two customers were like driving twins. Obviously, if this were the only pair of driving twins in a dataset of more than 13, observations, it would not be worth commenting on. But it is not the only pair. There are 22 four-car Calibri customers in the dataset [ 11 ]. All of them have a Cambria driving twin: a Cambria-fonted customer whose mileage for all four cars is greater than theirs by less than 1, miles. Here are some examples:. Because there are so few policies with four cars, finding these twins requires minimal effort [ 12 ].

But there are twins throughout the data, and you can easily identify them for three-car, two-car, and unusual one-car customers, too. There are 12 such policies for Calibri customers in the dataset…and 12 such policies for Cambria customers in the dataset. Once again, each Calibri observation has a Cambria twin whose baseline mileage exceeds it by less than 1, miles.

This is again true for every car on the policy:. To see a fuller picture of just how similar these Calibri and Cambria customers are, take a look at Figure 5, which shows the cumulative distributions of baseline miles for Car 1 and Car 4. Within each panel, there are two lines, one for the Calibri distribution and one for the Cambria distribution. The lines are so on top of each other that it is easy to miss the fact that there are two of them:. We ran 1 million simulations to determine how often this level of similarity could emerge just by chance. For details, see this footnote: [ 13 ]. These data are not just excessively similar. They are impossibly similar. Anomaly 4: No Rounding in Cambria Observations. As mentioned above, we believe that a random number between 0 and 1, was added to the Calibri baseline mileages to generate the Cambria baseline mileages.

And as we have seen before, this process would predict that the Calibri mileages are rounded, but that the Cambria mileages are not. We thank The Economist for spotting two typos in Figure 6, which we have now fixed. Original version:. The evidence presented in this post indicates that the data underwent at least two forms of fabrication: 1 many Time 1 data points were duplicated and then slightly altered using a random number generator to create additional observations, and 2 all of the Time 2 data were created using a random number generator that capped miles driven, the key dependent variable, at 50, miles. A single fraudulent dataset almost never provides enough evidence to answer all relevant questions about how that fraud was committed.

And this dataset is no exception.

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