Fact Sheet
Statistics in SJMC
While many may not think of journalism and strategic communications as math-heavy coursework – Statistics is now what sets students apart. An understanding of statistics can be the difference between landing the job and not making it past the interview. This spring, SJMC has focused on emphasizing rudimentary skills in data and statistics in core classes. Topics such as mean, median, margin of error, etc., are important for students as they enter a field focused on engagement growth and social numbers.
Topics
Mean/Median/Mode: Ways to find the “average person” in a dataset, usually for a large group of people
Margin of Error: The percentage point(s) of which the sample could vary
Sample size/Representative Samples: How many people were sampled and if they were sampled correctly
Correlation vs. Causation: Things may correlate, but you need to prove through statistics for it to be causation
Percentage decline/incline: The amount of which a percentage goes up or down
Common problems
Mean: One outlier can skew data up or down (e.g., salaries, net wealth)
Median: The number in the middle of an ordered dataset
Margin of Error: May be overlooked, important when the dataset is smaller
Sample size/representative samples: People may take convenience samples, making them not truly random, and they are not representative of the population at large
Correlation vs. Causation: Without statistical knowledge, it can be hard to distinguish, and it may lead to misinformation being spread.
Percentage increase/decrease: Can be confusing; people may only look at the difference and not the percentage of increase/decrease
Where it’s used best
Audience Analytics: In this New York Times page, it displays how analytics influence what they write and who they write for. It also attracts advertisers for a specific audience of people. They illustrate their growth statistics well and in a digestible way. This page uses a large sample size of readership while also showing what stories they saw an incline/decline in page views.
Data journalism: This Wall Street Journal article breaks down the content of Elon Musk’s Tweets and his turn in politics. This kind of journalism and understanding of breaking down numbers has been highly sought after by employers. Data journalism commonly looks at the percentage increase/decrease along with the mean/median/mode of a population. In this example, they mapped out Elon Musk's average (mean) tweet per day and the content.
Where they dropped the ball:
This chart by Fox News shows how data can be misleading for media consumers. Although there is only a 4% difference between the two issues, it looks like a humongous jump.
When data is scaled in such a small manner (by 2’s), it leads the audience to think there is a larger difference, which may impact voting decisions.
This shows how sampling and representation of a sample impact the credibility of a chart.
