Most people find statistics to be complicated, confusing, and just generally frustrating. One of the biggest causes of confusion is the complicated vocabulary that is associated with stats. Frankly, it sometimes seems that stats terms were made to be intentionally complicated. In fact, some concepts seem perfectly understandable when described inplain English, but seem incomprehensible when described in stats lingo.
With this in mind, I decided to compile what I think are the top ten most confusing stats terms and describe them in plain English, to clear-up some of the confusion that surrounds them. Initially, this was intended to be a single blog, but I soon realized far too many words are required to adequately explain this “Top Ten” list in one entry, so I’ve decided to present them over ten entries. I hope this will allow me to offer thorough explanation and examples.
Without further delay...
The Top Ten Confusing Stats Terms: #10- Standard Deviation
Standard deviation is a descriptive statistic that is used to understand the distribution of a dataset. It is often reported in combination with the mean (or average), giving context to that statistic. Specifically, a standard deviation refers to how much scores in a dataset tend to spread-out from the mean.
A small standard deviation (relative to the mean score) indicates that the majority of individuals (or data points) tend to have scores that are very close to the mean (see figure below). In this case, cases may look clustered around the mean score, with only a few scores farther away from the mean (probably outliers).
By contrast, a sample with a large standard deviation (relative to the mean score) tends to have cases that are more widely spread-out from the mean (see figure on right), perhaps with only a few cases actually having scores that fall close to the mean.
You may be wondering to yourself: “Why should I care about the standard deviation?” The answer to that question is context. To really understand the basic characteristics of a dataset, you must put your statistics in context.
Allow me to demonstrate:
For the sake of demonstration, imagine we have two samples of chocolate cake eaters, each sample with 10 people, self-reporting how many pieces of chocolate cake they've eaten in the last seven days.
- In dataset #1, we have five people that report eating 4 pieces of cake and five people that report eating 6 pieces of cake, for a mean of 5 pieces of cake
- (4+4+4+4+4+6+6+6+6+6)/10 = 5
- In dataset #2, we have five people that report eating 0 piece of cake and five people that report eating 10 pieces of cake, for a mean of 5 pieces of cake
- (0+0+0+0+0+10+10+10+10+10)/10 = 5.
Looking at the mean score alone would leave us to believe that these two datasets of people have the
same chocolate cake eating habits (eating about
5 pieces per person), but would we ever come to that conclusion, given access to the full information that we have here?
Of course not. Instead we would probably say that the mean of 5 pieces per person seems to describe sample #1 reasonably, but not-so-much for sample #2, as it seems to be composed of people with more extreme chocolate cake eating habits (either eating a whole lot of chocolate cake in a week or having none at all).
In this case the datasets are mathematically similar, but the mean of the two samples is somewhat deceptive. In fact, the mean statistic can be a deceptive little bugger in general, when it is not presented in context. That is where a standard deviation comes in!
Now, you might be thinking: “Why not just look at the raw data and come to that conclusion? After all, you just came to that conclusion without ever talking about the standard deviation!”
Well, that is fine as long as you only have ten people in each sample AND as long as your sample is so neatly, cleanly, and clearly organized into moderate values and extreme values, as it is here. If that is the case, then you likely can get a perfectly firm grasp on your data without ever knowing the standard deviation! Unfortunately, data is rarely that clear and samples sizes can be in the hundreds, thousands, or even millions, making it impossible to "eye-ball" the data and draw reliable conclusions.
When these instances arise (which will be almost every time you work with data), your friendly standard deviation can give you the context you need. Let's consider the standard deviations of our chocolate cake datasets. Knowing that larger values of standard deviation are indicative of more points "spread" away from the mean, compared to smaller standard deviation values (as discussed in our first paragraph), which sample (#1 or #2) would you expect to have a larger standard deviation?...
(I'll pause while you ponder your answer and then answer out-loud at a volume just loud enough to make the person sitting next to you wonder if you are stable and if it is safe to sit next to you...)
To calculate the standard deviation for your data, well let's face it, all you need to do is use SPSS (or any other statistical software package). In SPSS, you can obtain the standard deviation by:
- In the menu bar, go to: Analyze -> Descriptives ->Options -> check the Standard Deviation Box, if it isn't already [should be by default]-> click OK-> move the variable you want to calculate from the list of variables in the left dialogue box to the empty dialogue box on the right-> click OK.
If you happen to want to calculate by hand, you simply:
- Subtract your mean score from every person's actual (observed) score
- Square those difference scores for each person
- Add those values together for the whole sample
- Divide that sum by the number of cases in your data (10 in our case)
- Finally, calculate the square root of the number calculate in step #4
For those of you that like formulas:
Now, back to our example. You will recall that I asked you to guess which sample you would expect to have the larger standard deviation (#1 or #2). Well, if you said sample #2, you would be correct!
- In dataset #1, we have five people that report eating 4 pieces of cake and five people that report eating 6 pieces of cake, for a mean of 5 pieces of cake ([4+4+4+4+4+6+6+6+6+6]/10=5).
- Mean =5; Standard Deviation = 1
- In dataset #2, we have five people that report eating 0 piece of cake and five people that report eating 10 pieces of cake, for a mean of 5 pieces of cake ([0+0+0+0+0+10+10+10+10+10]/10=5).
- Mean = 5; Standard Deviation = 5
Note: You will almost never see the mean and standard deviation with the same value. This example was made intentionally extreme for demonstration purposes, but clearly you wouldn't typically have your entire sample fall into either all 0's or all 10's (unless it is categorical data, in which case there is no need for means and standard deviation scores).
From this example , we can see that the standard deviation is critical to understanding your data, by putting your mean statistic in context, in this case indicating that the mean for the dataset #2 is not a very meaningful or useful statistic for understanding the eating tendencies of individuals in that dataset.
A few closing notes about standard deviations:
- A dataset's variance can be calculated by simply squaring the standard deviation.
- Variance = (standard deviation)2
- Variance will be the topic of "Top Ten Confusing StatsTerms" blog #7, so you'll see in two weeks why we care about both for different reasons...
- One standard deviation above and below the mean is expected to include about 68% of the participant's scores in your dataset (assuming your distribution is normal).
- Two standard deviations above and below the mean would be expected to include 95% of the values in your dataset (assuming your distribution is normal).
- Three standard deviations above and below the mean would be expected to include 99.7% of the values in your dataset (assuming your distribution is normal).
- As eluded to earlier, standard deviations should only be calculated for interval data (also true for a mean score).
- Interval data is data that is numeric and hold an intrinsic and consistent value between values (such as 1 to 2 represents an equal increase to 2 to 3 or 3 to 4..etc).
Jeremy Taylor | 
Reader Comments (12)
Preparing to go for a scholarship test, just found your blog.
It refreshes what I learnt years back, and I would also like to state that someone new to statistics won't find it difficult.
You did a great job.
Thanks Ben!!!
I have studied statistics in 1990's, learned how to calaculate Standard deviation but never learned what is the purpose of it. Thanks to your simple explanation it is now very clear to me, great. It is never too tate to learn :-)
I'm glad it was helpful Sohail! Thanks for the kind words!
I'm teaching statistics to high school students and am thrilled to have found your blog. Your explanation is better than any I could have come up with and beats the heckle out of the text book.
Thank you!
Thanks! I'm glad it is helpful!
This was really helpful! :D I'm waiting for #7 Variance. I don't understand what is the difference between Variance and Standard Deviation.
This was just AMAZING! Thank you SO much for this clear cut explanation. I am taking statistics now in University and I can tell you I feel SO dumb when the professor is talking and I don't want to appear dumb and ask him questions or my friends as they will just give me that are you for real look. Now I can understand standard deviation and feel good about myself and knowledgable and work towards increasing my confidence and self esteem. Thank you once gain SOOOO much. :)
Glad it was helpful!
Thanks for the useful details Jeremy. I know that SD is measured relative to the mean. One may need to say that SD is low or high (relative to the mean), when can one say low or high? i.e if mean is 3, is 1 low...if so is 1.5 low or high?....etc
Hi Thanks so much it is really useful to understand and make some sense the reasons for calculating SD.
I'm a bit stumped on T tests if you have any more goodies to share! Warm regards,
Leah
I'm glad it was helpful, Leah! What specific questions do you have about t-tests? I'll be happy to try to help!