It is not only about understanding about statistics, it is also about implementing the correct statistical approach or method. In this brief article I will showcase some common statistical blunders that we generally make and how to avoid them.
To make this information simple and consumable I have divided these errors into two parts:
Data Visualization Errors (Erroneous Graphs): This is one area that can give a nightmare to both the parties the presenter as well as the audience. Incorrect data presentation can skew the inference and can leave the interpretation at the mercy of the audience.
Pie Charts: “Get back to the kitchen and make me some good Pie”
Pie charts are considered to be the best graph when you want to show how the categorical values are broken. However, they can be seriously deceptive or misleading. Below are some quick points to remember when looking at the Pie Charts:
Bar Graphs: “Let’s make a Bar Graph not a Bawaaaah Graph”
It’s a great graph to show the categorical data by the number or percent for a particular group. Points to consider when examining a Bar Graph:
Time Charts: “What time is it?”
A time charts is used to show how the measurable quantities change by time.
Histograms: “The Binning Man”
It is a good practice to check the scale used for the vertical axis frequency (relative or otherwise), especially when the results are showed down through the use of inappropriate scale
Statistical Blunders Galore: This is probably a “no non-sense zone” where one would not want to make false assumptions or erroneous selections. Statistical errors can be a costly affair, if not checked or looked into it carefully.
Biased Data:
Bias in statistics can be termed as over or underestimating the true value. Below are some most common sources or reasons for such errors.
No Margin of Error: “No there isn’t any margin of error on spelling tests, it is not mathematics”
This is a great way to understand the potential miscalculation or change in circumstance that can result in a sampling error and ensures that the result from a sample study is close to the number that can be expected from the entire population. It is a good idea to always look for this statistics to ensure that the audiences are not left to wonder about the accuracy of the study.
Non Random Sample: Nonrandom samples are biased, and their data cannot be used to represent any other population beyond themselves. It is pivotal to ensure that any study is based on the random sample and if it isn’t you are about to get into a big trouble. “Go and hide somewhere!”
Correlation is not Causation:
Besides the above statement correlation is one statistic that has been misused more than being used. Below are the few reasons that makes me believe the misuse part of this statistic.
Correlation applies only to two numerical variables, such as weight and height, call duration and hold time, test scores for a subject and time spent studying that subject etc. So, if you hear someone say, “It appears that the study pattern is correlation with gender,” you know that’s statistically incorrect. Study pattern and gender might have some level of association but they cannot be correlated in the statistical sense.
Correlation helps to measure the strength and the direction of a linear relationship. If the correlation is weak, once can say that there is no linear relationship but that doesn’t mean that there is no other type of relationship that might exist.
Botched Numbers: One should not believe in everything that appears with statistics. As we know error appears all the time (either by design or by error), so look for the below points to ensure that there are no botched numbers.
Being a consumer of the information it is your job to identify shortcomings within the data and analysis presented to avoid that “oops” moment. Statistics are nothing but simple calculations that are smartly used by people who are either ignorant or don’t want you to catch them to make their story interesting. So to be a certified skeptic wear your statistic glasses.
Comment
Thanks Philip Morgan for liking the article and I second your pet peeve about "Correlation is not Causation" not only media, I have seen seasoned analytical professionals making this fundamental mistake.
Glad to see that you hit my pet peeve: "Correlation is not Causation" (so many studies bandied about in the media make this major mistake!)
One of my favorite quotes (attributable to whom, I do not know) was once posted on an office door where I work. It read: "Studies show that the numbers we made up were just as effective as the numbers you calculated" (Love this!).
Final note: Never admit that you 'made-up' numbers (the numbers were always, ALWAYS.... generated) :-)
© 2017 Data Science Central Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
You need to be a member of Data Science Central to add comments!
Join Data Science Central