*Section author: Danielle J. Navarro and David R. Foxcroft*

# Measures of central tendency¶

Drawing pictures of the data, as I did in Fig. 20, is an
excellent way to convey the “gist” of what the data is trying to tell you.
It’s often extremely useful to try to condense the data into a few simple
“summary” statistics. In most situations, the first thing that you’ll want
to calculate is a measure of **central tendency**. That is, you’d like to
know something about where the “average” or “middle” of your data lies.
The three most commonly used measures are the mean, median and mode.
I’ll explain each of these in turn, and then discuss when each of them
is useful.

## The mean¶

The **mean** of a set of observations is just a normal, old-fashioned average.
Add all of the values up, and then divide by the total number of values. The
first five AFL winning margins were 56, 31, 56, 8 and 32, so the mean of these
observations is just:

Of course, this definition of the mean isn’t news to anyone. Averages (i.e., means) are used so often in everyday life that this is pretty familiar stuff. However, since the concept of a mean is something that everyone already understands, I’ll use this as an excuse to start introducing some of the mathematical notation that statisticians use to describe this calculation, and talk about how the calculations would be done in jamovi.

The first piece of notation to introduce is *N*, which we’ll use to refer to
the number of observations that we’re averaging (in this case *N* = 5`). Next,
we need to attach a label to the observations themselves. It’s traditional to
use *X* for this, and to use subscripts to indicate which observation we’re
actually talking about. That is, we’ll use *X*_{1} to refer to the first
observation, *X*_{2} to refer to the second observation, and so on all the
way up to *X*_{N} for the last one. Or, to say the same thing in a slightly
more abstract way, we use *X*_{i} to refer to the *i*-th observation. Just
to make sure we’re clear on the notation, the following table lists the 5
observations in the `afl.margins`

variable, along with the mathematical
symbol used to refer to it and the actual value that the observation
corresponds to:

the observation | its symbol | the observed value |
---|---|---|

winning margin, game 1 | X_{1} |
56 points |

winning margin, game 2 | X_{2} |
31 points |

winning margin, game 3 | X_{3} |
56 points |

winning margin, game 4 | X_{4} |
8 points |

winning margin, game 5 | X_{5} |
32 points |

Okay, now let’s try to write a formula for the mean. By tradition, we use *X̄*
as the notation for the mean. So the calculation for the mean could be
expressed using the following formula:

This formula is entirely correct but it’s terribly long, so we make use
of the **summation symbol** Σ to shorten it.[1] If I want to add up the
first five observations I could write out the sum the long way, *X*_{1} +
*X*_{2} + *X*_{3} + *X*_{4} + *X*_{5} or I could use
the summation symbol to shorten it to this:

Taken literally, this could be read as “the sum, taken over all *i* values from
1 to 5, of the value *X*_{i}”. But basically what it means is “add up the
first five observations”. In any case, we can use this notation to write out
the formula for the mean, which looks like this:

In all honesty, I can’t imagine that all this mathematical notation helps
clarify the concept of the mean at all. In fact, it’s really just a fancy way
of writing out the same thing I said in words: add all the values up and then
divide by the total number of items. However, that’s not really the reason I
went into all that detail. My goal was to try to make sure that everyone
reading this book is clear on the notation that we’ll be using throughout the
book: *X̄* for the mean, Σ for the idea of summation, *X*_{i} for the
*i*-th observation, and *N* for the total number of observations. We’re going
to be re-using these symbols a fair bit so it’s important that you understand
them well enough to be able to “read” the equations, and to be able to see that
it’s just saying “add up lots of things and then divide by another thing”.

## Calculating the mean in jamovi¶

Okay, that’s the maths. So how do we get the magic computing box to do
the work for us? When the number of observations starts to become large
it’s much easier to do these sorts of calculations using a computer. To
calculate the mean using all the data we can use jamovi. The first step
is to click on the `Exploration`

button and then click `Descriptives`

.
Then you can highlight the `afl.margins`

variable and click the `→`

to
move it across into the `Variables`

box. As soon as you do that a Table
appears on the right hand side of the screen containing default
`Descriptives`

information; see Fig. 7.

As you can see in Fig. 7, the mean
value for the `afl.margins`

variable is **35.30**. Other information
presented includes the total number of observations (*N* = 176), the number
of missing values (none), and the Median, Minimum and Maximum values for
the variable.

## The median¶

The second measure of central tendency that people use a lot is the
**median**, and it’s even easier to describe than the mean. The median
of a set of observations is just the middle value. As before let’s
imagine we were interested only in the first 5 AFL winning margins: 56,
31, 56, 8 and 32. To figure out the median we sort these numbers into
ascending order:

**32**, 56, 56

From inspection, it’s obvious that the median value of these 5 observations is 32 since that’s the middle one in the sorted list (I’ve put it in bold to make it even more obvious). Easy stuff. But what should we do if we are interested in the first 6 games rather than the first 5? Since the sixth game in the season had a winning margin of 14 points, our sorted list is now:

**31**,

**32**, 56, 56

and there are *two* middle numbers, 31 and 32. The median is defined as
the average of those two numbers, which is of course 31.5. As before,
it’s very tedious to do this by hand when you’ve got lots of numbers. In
real life, of course, no-one actually calculates the median by sorting
the data and then looking for the middle value. In real life we use a
computer to do the heavy lifting for us, and jamovi has provided us with
a Median value of 30.50 for the `afl.margins`

variable
(see Fig. 7).

## Mean or median? What’s the difference?¶

Knowing how to calculate means and medians is only a part of the story. You also need to understand what each one is saying about the data, and what that implies for when you should use each one. This is illustrated in Fig. 8. The mean is kind of like the “centre of gravity” of the data set, whereas the median is the “middle value” in the data. What this implies, as far as which one you should use, depends a little on what type of data you’ve got and what you’re trying to achieve. As a rough guide:

- If your data are nominal scale you probably shouldn’t be using either the mean or the median. Both the mean and the median rely on the idea that the numbers assigned to values are meaningful. If the numbering scheme is arbitrary then it’s probably best to use the Mode instead.
- If your data are ordinal scale you’re more likely to want to use the median than the mean. The median only makes use of the order information in your data (i.e., which numbers are bigger) but doesn’t depend on the precise numbers involved. That’s exactly the situation that applies when your data are ordinal scale . The mean, on the other hand, makes use of the precise numeric values assigned to the observations, so it’s not really appropriate for ordinal data.
- For interval and ratio scale data either one is generally acceptable. Which one you pick depends a bit on what you’re trying to achieve. The mean has the advantage that it uses all the information in the data (which is useful when you don’t have a lot of data). But it’s very sensitive to extreme, outlying values.

Let’s expand on that last part a little. One consequence is that there are systematic differences between the mean and the median when the histogram is asymmetric (skewed; see Skew and kurtosis). This is illustrated in Fig. 8. Notice that the median (right hand side) is located closer to the “body” of the histogram, whereas the mean left hand side) gets dragged towards the “tail” (where the extreme values are). To give a concrete example, suppose Bob (income $50,000), Kate (income $60,000) and Jane (income $65,000) are sitting at a table. The average income at the table is $58,333 and the median income is $60,000. Then Bill sits down with them (income $100,000,000). The average income has now jumped to $25,043,750 but the median rises only to $62,500. If you’re interested in looking at the overall income at the table the mean might be the right answer. But if you’re interested in what counts as a typical income at the table the median would be a better choice here.

## A real life example¶

To try to get a sense of why you need to pay attention to the differences between the mean and the median let’s consider a real life example. Since I tend to mock journalists for their poor scientific and statistical knowledge, I should give credit where credit is due. This is described in an excellent article Housing bubble debate boils over:

Senior Commonwealth Bank executives have travelled the world in the past couple of weeks with a presentation showing how Australian house prices, and the key price to income ratios, compare favourably with similar countries. “Housing affordability has actually been going sideways for the last five to six years,” said Craig James, the chief economist of the bank’s trading arm, CommSec.

This probably comes as a huge surprise to anyone with a mortgage, or who wants a mortgage, or pays rent, or isn’t completely oblivious to what’s been going on in the Australian housing market over the last several years. Back to the article:

CBA has waged its war against what it believes are housing doomsayers with graphs, numbers and international comparisons. In its presentation, the bank rejects arguments that Australia’s housing is relatively expensive compared to incomes. It says Australia’s house price to household income ratio of 5.6 in the major cities, and 4.3 nationwide, is comparable to many other developed nations. It says San Francisco and New York have ratios of 7, Auckland’s is 6.7, and Vancouver comes in at 9.3.

More excellent news! Except, the article goes on to make the observation that:

Many analysts say that has led the bank to use misleading figures and comparisons. If you go to page four of CBA’s presentation and read the source information at the bottom of the graph and table, you would notice there is an additional source on the international comparison – Demographia. However, if the Commonwealth Bank had also used Demographia’s analysis of Australia’s house price to income ratio, it would have come up with a figure closer to 9 rather than 5.6 or 4.3

That’s, um, a rather serious discrepancy. One group of people say 9, another says 4-5. Should we just split the difference and say the truth lies somewhere in between? Absolutely not! This is a situation where there is a right answer and a wrong answer. Demographia is correct, and the Commonwealth Bank is wrong. As the article points out:

[An] obvious problem with the Commonwealth Bank’s domestic price to income figures is they compare average incomes with median house prices (unlike the Demographia figures that compare median incomes to median prices). The median is the mid-point, effectively cutting out the highs and lows, and that means the average is generally higher when it comes to incomes and asset prices, because it includes the earnings of Australia’s wealthiest people. To put it another way: the Commonwealth Bank’s figures count Ralph Norris’ multi-million dollar pay packet on the income side, but not his (no doubt) very expensive house in the property price figures, thus understating the house price to income ratio for middle-income Australians.

Couldn’t have put it better myself. The way that Demographia calculated the ratio is the right thing to do. The way that the Bank did it is incorrect. As for why an extremely quantitatively sophisticated organisation such as a major bank made such an elementary mistake, well… I can’t say for sure since I have no special insight into their thinking. But the article itself does happen to mention the following facts, which may or may not be relevant:

[As] Australia’s largest home lender, the Commonwealth Bank has one of the biggest vested interests in house prices rising. It effectively owns a massive swathe of Australian housing as security for its home loans as well as many small business loans.

My, my.

## Mode¶

The mode of a sample is very simple. It is the value that occurs most
frequently. We can illustrate the mode using a different AFL variable:
who has played in the most finals? Open the `aflsmall_finalists`

data
set and take a look at the `afl.finalists`

variable, see
Fig. 9. This variable contains the names of
all 400 teams that played in all 200 finals matches played during the
period 1987 to 2010.

What we *could* do is read through all 400 entries and count the number
of occasions on which each team name appears in our list of finalists,
thereby producing a **frequency table**. However, that would be mindless
and boring: exactly the sort of task that computers are great at. So
let’s use jamovi to do this for us. Under `Exploration`

→ `Descriptives`

click the small check box labelled `Frequency tables`

and you should get
something like Fig. 10.

Now that we have our frequency table we can just look at it and see
that, over the 24 years for which we have data, Geelong has played in
more finals than any other team. Thus, the mode of the `afl.finalists`

data is “Geelong”. We can see that Geelong (39 finals) played in
more finals than any other team during the 1987 to 2010 period. It’s also
worth noting that in the `Descriptives`

Table no results are calculated
for Mean, Median, Minimum or Maximum. This is because the
`afl.finalists`

variable is a nominal text variable so it makes no
sense to calculate these values.

One last point to make regarding the mode. Whilst the mode is most often
calculated when you have nominal data, because means and medians are useless
for those sorts of variables, there are some situations in which you really do
want to know the mode of an ordinal, interval or ratio scale variable. For
instance, let’s go back to our `afl.margins`

variable. This variable is
clearly ratio scale (if it’s not clear to you, it may help to re-read section
Scales of measurement), and so in most situations
the mean or the median is the measure of central tendency that you want. But
consider this scenario: a friend of yours is offering a bet and they pick a
football game at random. Without knowing who is playing you have to guess the
*exact* winning margin. If you guess correctly you win $50. If you don’t you
lose $1. There are no consolation prizes for “almost” getting the right answer.
You have to guess exactly the right margin. For this bet, the mean and the
median are completely useless to you. It is the mode that you should bet on. To
calculate the mode for the `afl.margins`

variable in jamovi, go back to that
data set and on the `Exploration`

→ `Descriptives`

screen you will see you
can expand the section marked `Statistics`

. Click on the checkbox marked
`Mode`

and you will see the modal value presented in the `Descriptives`

Table, as in Fig. 11. So the 2010 data suggest you
should bet on a 3 point margin.

[1] | The choice to use Σ to denote summation isn’t arbitrary. It’s the Greek
upper case letter sigma, which is the analogue of the letter S in that
alphabet. Similarly, there’s an equivalent symbol used to denote the
multiplication of lots of numbers, because multiplications are also called
“products” we use the Π symbol for this (the Greek upper case pi, which is
the analogue of the letter P). |