“It’s not statistics. It’s using Topsy (which had nothing to do w/ the actual newspaper article) in whatever way they see fit. 1

Without givin their methodology or an explanation what Topsy has to do with the work that PROFESSIONALS did at BrandWatch 2"

BrandWatch and I both engaged in statistical analysis as in organization and description of data. They employed data preparation via gathering the data and sorting it from tweets. I did it by examining aggregated data from a website. Both are forms of statistical analysis.

As for what Topsy “has to do with the work,” it has to do with getting data from a source. Just like BrandWatch is a source for the Newsweek.

Statistical analysis results from data analytics where data is collected, described, explored, hypothesized, and used in predictive manners. This is a three step process through which data is gathered, organized, described, tested, and modeled through hypothesis testing. However, not all analysis goes through these steps depending on the desired outcome and statistical need.

Statistics come in two forms: descriptive and inferential statistics. Nothing done by BrandWatch falls under inferential statistics which seek to conclude from data points. They are, in sum and total, descriptive. They describe a phenomenon, they do not establish the quality, kind, or significance of the phenomenon.

This conclusion is made by Newsweek’s writer without statistical testing, hypothesis, or even standardization. As such, I did not engage in this process either. Instead, I stuck to examination of the data on the descriptive level.

Companies do not always do quality, professional work either.

Claire Robsahm, a transgender developer, tweeted a series of tweets.

This guy isn’t very good at statistics. I can explain more in depth later.

Basically, it doesn’t matter that the majority were neutral. That’s not what the study was about.

Claire here essentially says that it does not matter that the majority of the presented data show that opinions about the persons were neutral when discussing if people are harassing.

“It was about harassment and negativity. The neutrals just serve to passively support the negatives or positives in any group.”

Of course, do note that Claire has yet to even discuss statistics. That would be because Claire is abundantly well aware that she has no foundation in statistics. Instead, Claire has to settle on discussing the descriptive argument instead of the inferential failures of the author in deducing without statistical testing.

So let’s employ some critical thinking skills as Claire basically told us:

It does not matter if people are favorable or neutral because the issue is about negativity and neutrality is passive support of negativity.

“Which this “analysis” doesn’t really mention. His bit about who receives the most harassment also makes little sense.

Like, my biggest problem here is that he talks about how Greyson received more negativity per capita than any of the women.

He neglects to mention that Greyson receives the smallest amount of mentions AT ALL.

Per capita can be a very important thing, but in this case, it’s not as relevant as this individual gives it credence.”

What Claire does not discuss is the problem with focusing on per capita information. This is where Claire, had she understood statistics, could know why we use proportional data with absolute data.

Absolute data is typically skewed to populational extremes. Take for example income information. Most people make about $50,000, give or take. However, there are some people who make $100,000,000,000. What each person spends on will be different. The smaller amounts of money must spend more money on necessary items like food, water, etc. So, we look at both the absolute data and the proportional data.

Another way of thinking about it is crime and arrests. Whites are a larger population group and commit more crimes when looking at absolute crime data. However, when adjusting for population, we find that Blacks tend to account for more crime in terms of rates. Which group does more crime? Well, the answer is, “It depends on the measure.”

Whites account for more absolute crime (6.5 million of 9.3 million) but Blacks account for a larger amount of arrests and crime when taking into account the relatively small population.

A third way of considering it is the HIV/AIDS epidemic. Most absolute cases are in Sub-Saharan Africa. However, relative to population, men who have sex with men is a smaller population therefore is more impacted by HIV/AIDS as a population.

Additionally, extremes skew the average. If a person receives more tweets, they’re more likely to be exposed to positive and negative tweets by the nature of the behavioral beast. You will have few on the extreme and lots in the middle.

But we’re going to get into the “per capita” thing after the break.