The *mode* of a list of data values is simply the most common value (or values … if any). When data is grouped (binned) as in a histogram, we normally talk only about the *modal class* (the class, or group, with the greatest frequency), because we don’t know the individual values. But some sources teach a formula for finding (actually just *estimating*) the mode. We’ve had a number of questions about that formula.

## The formula

I had never heard of such a formula until 2007, when a question was asked about applying it in a special case. That answer wasn’t archived, but when we got another question about it a year later, it was time to publish what I had figured out. Here is the 2008 question, from Saptarshi:

Different Formulas for Calculating Mode I am a M.B.A student. Our teacher tells a formula to find out mode, that is Z=L1+(F1-F0)/(2F1-F0-F2)*i where: L1 = lower limit of modal class F1 = modal class frequency. F2 = just after the modal class frequency. F0 = just previous the modal class frequency. i = class interval. Z = the mode value. But I saw in most of cases the highest frequency is the mode. They don't use that formula. (I saw that when searching about mode in google). So why we need that formula? Can you please explain me.

I think he is saying that whereas he was taught this formula for the mode, most sources he found online do as I have usually seen, identifying only the class with the greatest frequency as the mode (actually the *modal class*). So, why was he taught this formula, and what does it mean?

The formula, which I now find more easily around the Web than I could back then, takes several forms. His, in more readable format, is $$Z = L_1 + \frac{F_1 – F_0}{2F_1 – F_0 – F_2}\cdot i.$$ The form we had previously been asked about was a little different: $$Z = L_1 + \frac{d_1}{d_1 + d_2}\cdot i,$$ where \(d_1\) and \(d_2\) are the differences between the frequency of the modal class and those of its nearest neighbors.

I started this answer by stating what the formula does, and showing the two formulas to be equivalent:

This formula gives alinear interpolation to estimate the actual value of the modefrom grouped data; otherwise, all you really know is the modal class (which is sufficient for many purposes). Your formula can be written differently if we take d1 = F1 - F0 (difference between modal class and previous class) d2 = F1 - F2 (difference between modal class and next class) Then d1 + d2 = (F1 - F0) + (F1 - F2) = 2F1 - F0 - F1, so the formula is Z = L1 + d1/(d1 + d2) * i

I have never found an explanation of the formula in a mathematical source that explains its proper derivation and the conditions under which it is valid; there are several sites that explain it after-the-fact as I will do below, but most sources I find are at a basic level where they just state the formula and tell students to use it. Most, in fact, just state that it gives the mode, whereas, as stated above, it is really only a *guess* — an estimate of what the actual mode *might* be, based on the shape of the histogram. We don’t really know how the data are distributed within any of the classes, so it is impossible to know the actual mode; it may not even be in the modal class. On the other hand, the actual mode may just reflect that some random data points happen to be identical; a number based on the overall shape may really be more meaningful! So this is a valid concept, at least in some situations.

One source that gives this formula with a proper description is

Math Is Fun: Mean, Median and Mode from Grouped Frequencies

which says, under “Estimating the Mode from Grouped Data”,

We can easily find the modal group (the group with the highest frequency), which is

61 – 65.We can say “the

modal groupis 61 – 65″.But the actual Mode may not even be in that group! Or there may be more than one mode. Without the raw data we don’t really know.

But, we can

estimate the Modeusing the following formula:

Estimated Mode=\(L + \frac{f_m − f_{m-1}}{(f_m − f_{m-1}) + (f_m − f_{m+1})} × w\)

where:

- L is the lower class boundary of the modal group
- f
_{m-1}is the frequency of the group before the modal group- f
_{m}is the frequency of the modal group- f
_{m+1}is the frequency of the group after the modal group- w is the group width

I gave some references, and then quoted what I had said in answering the 2007 question:

The formula these sites give, with definitions of the variables, is (using the second site's version): When data are already grouped in a frequency distribution, we can assume that the mode is located in the class with the most items. In order to determinea single value for the modefrom this modal class, we use mode = LBMo + [d1 /(d1+d2)] (Width) where LBMo = lower boundary of the modal class Width = width of the modal class interval d1 = frequency of the modal class minus the frequency of the class directly below it d2 = frequency of the modal class minus the frequency of the class directly above it Note that d1 and d2 relate to the classes on the left and on the right in the histogram. If there is no class on the left, then you can imagine a class with frequency zero. Then the formula applies easily.

Note that this source rightly said the formula only gives “*a* single value for the mode”, not “*the* actual value of the mode”.

My last paragraph above dealt with the issue the earlier questioner had been asking about.

The purpose of this formula is to identify one value within the modal class that seems likely to be the peak of the curve if you smoothed out the histogram. It does this by taking the value within the interval whose distance from the class on either side is proportional to how much less the frequency is on either side. You can see this by rewriting the formula: mode - L1 d1 --------- = ------- Width d1 + d2

That is, the distance from the lower bound (left end) of the modal class, as a fraction of the width of the modal class, is the ratio of the left difference to the sum of the differences.

In thinking about this relationship, I saw a graphical meaning to the formula (which I now see on various other sites; I’m sure I’m not the first to have seen it):

There is a simple geometrical way you could find this point. Just draw lines from the top corners of the modal bar to the near corners of the neighboring bars, and the mode estimate lies at the intersection: +---------+ | \ / |d2 d1| X | | / : +---------+ | / : | | +---------+ : | | | | : | | | | : | | | | : | | | | : | | +---------+-----:---+---------+ L1 mode |<------->| width

This puts the estimated mode closer to the higher neighboring bar, which makes sense. (I’ll have more to say about that below.) If you’re not sure how this relates to the proportion I wrote, look for a pair of similar triangles …

I closed with an example (again, quoted from my response a year earlier, and using that writer’s notation, where “85<91” meant the six numbers starting at 85, and less than 91):

For an example, take these classes: 85<91 10 91<97 8 97<103 3 103<109 8 109<115 0 115<121 7 The modal class is 85<91. LBmo = 85 width = 6 d1 = 10 - 0 = 10 (since the frequency on the left is 0) d2 = 10 - 8 = 2 (since the frequency on the right is 8) mode = LBMo + [d1 /(d1+d2)] (Width) = 85 + (10/12)(6) = 85 + 5 = 90 This is 5 from the left and 1 from the right, a ratio of 5:1, while the differences in frequency are 10:2.

## The “mode” depends on the classes

The next question about this formula was in 2015, from Gaurav:

Mode's Fickle Formula? The formula for mode isnot telling me the actual mode. In fact, after grouping data, I have found many situations wherethe mode changes. For example, given these data: 1, 1, 1, 1, 2, 3, 3, 3, 4, 4, 4 The mode is 1. But after grouping data, as below, the mode becomes approximately 3.3: CLASS FREQUENCY 1-3 5 3-5 6 Why does the mode of data change like this?

It appears that Gaurav had not been taught that the formula gives only a *guess* at the mode, and can’t be expected to give the actual mode, since it doesn’t have access to the actual data. But the question provided a good opportunity to examine more closely what the formula actually does. I replied:

The formula you have presumably been given for the mode of grouped data does not necessarily give the actual mode. Rather, it gives you a guess that is considered reasonable under some conditions.When you group data, you lose information, so you should expect not to be able to recover detail using any formula. In other words, the mode didn't change; you just guessed the mode from insufficient data. I don't actually know of any theoretical basis for the formula that would make it reasonable to expect it to be correct for some particular kind of data (e.g., approximately normal). But given the questions that we math doctors routinely see about this subject, it appears that it is commonly taught without explaining what the formula really is: an approximation, at best.

I gave a link to the answer above, to make sure we were talking about the same formula. Then I showed how the actual data provided (in the form of a “dot plot”) compare to the histogram:

Note that your data are not normally distributed, so it is not at all surprising that the formula would not work. Also, the actual data (*'s) and the grouped data (bars) look quite different: +---+ +---+ | |* | | |* |* *| |* |* *| |* *|* *| --+---+---+-- 1 2 3 4

Looking at that, we see that the mode of the actual data is not even in the modal class; this is because the data are not smoothly distributed, so the grouping changes its character. (My guess is that the formula is considered valid, as I suggested, for normally distributed data; it would be at least reasonable for a smooth and symmetrical distribution.)

We should check his work with the formula. Using the formula in the first form I showed above, $$Z = L_1 + \frac{F_1 – F_0}{2F_1 – F_0 – F_2}\cdot i,$$ we have \(L_1 = 2.5, F_0 = 5, F_1 = 6, F_2 = 0, i = 2\) so $$Z = 3 + \frac{6 – 5}{2\cdot 6 – 5 – 0}\cdot 2 = 3 + \frac{1}{7}\cdot 2 = 3.29.$$ Here I took \(L_1\) to be 3, the lower class *limit *as stated in the first form I quoted above, rather than 2.5, the lower class *boundary*, as in most versions I have found, in order to get his answer. I think the latter is the proper definition of the variable; I hadn’t noticed this discrepancy until now.

Gaurav asked another question:

Why would the mode of grouped data depend on the frequency of pre- and post-modal classes?

This is essentially asking for a deeper explanation of the formula. I replied:

The page I referred you to explains the formula as well as I can. The basic idea is thatif you have data that looks like a normal distribution(one symmetrical hump), but group the data,the classes on either side would be asymmetrical if the actual mode is not centered in the modal class; so looking at the adjacent classes can help estimate where the mode would be within the class. Here are two examples: symmetrical asymmetrical | | +-*-+ +--*+ * * |* |* *| |* * +-*-+ +-*-+ +-*-+ *| | *| * | | * +*--+ | +*--+ +*--+ | | +--*+ +-*-+ | | | * +---+---+---+---+---+ +---+---+---+---+---+ The symmetrical histogram should have its mode in the middle of the modal class. The histogram on the right -- with a higher bar on the right of the modal class -- should have its mode closer to the higher side. The formula does this in the simplest possible way.

I have made a histogram by binning the standard normal distribution in various ways, and found that the formula does give the mode quite accurately in that case. When I did the same for a triangular distribution, it was less accurate.

## What if there are two modal classes?

Here is a question from 2016:

Breaking the Mode How do you find the mode of this grouped data? data freq ------------- 10-14 5 15-19 12 20-24 12 25-29 10 30-34 4 I know the mode formula: Mo = L + (d1/(d1 + d2))*width I calculated its parts like this: L = 14.5 d1 = 12 - 5 = 7 d2 = 12 - 10 = 2 width = 24.5 - 14.5 = 10 But I'm confused about the last two. Should d2 = 12 - 12 = 0? Should the width be 5? From there, I went on to determine mode = 14.5 + 7/(7 + 2)*10 = 14.5 + 7.8 = 22.3 Is my work true?

The answer seems reasonable (it is at least within a modal class). But does the formula work when the “modal class” is double-wide?

First, we have to keep in mind that we don’t even know what it would mean for an answer to be correct, since we don’t know the actual data! But I answered:

The formula you are using does not really tell you "the mode"; it just makes a reasonable estimate of where the mode might be if the underlying distribution is, say, approximately normal.Since it is not exact in the first place, it probably doesn't matter much how you apply it in special cases.If you have been taught the formula without any further explanation, then you can't be expected to follow any particular rules for this case. I have never found a source for this formula that explains its theoretical basis, or the conditions under which it should be used, or how it applies in unusual cases (whichshould be an inference from the theory, if there were one). I've explained what I can guess from the formula, and from what sources I do find, here: Different Formulas for Calculating Mode http://mathforum.org/library/drmath/view/72977.html This explanation for itassumes generally that each class has the same width, so it doesn't quite apply when the "modal class" has twice the width of the others, which is the way you are treating it.

I made a suggestion, to rework the classes so they all have the same width, which is that of the double modal class:

I would probably rework the data so that there are fewer (equal width) classes, and just one modal class: data freq ------------ 5- 9 0 [added implied empty class] 10-14 5 15-19 12 20-24 12 25-29 10 30-34 4 data freq ------------ 5-14 5 [combined classes in pairs] 15-24 24 25-34 14 The formula applies directly now: L = 14.5 d1 = 24 - 5 = 19 d2 = 24 - 14 = 10 width = 24.5 - 14.5 = 10 Mo = L + (d1/(d1 + d2))*width = 14.5 + (19/(19 + 10))*10 = 21.05 Again, that seems to fit a little better with the derivation, but I don't think it makes much difference, since there is really no "correct" mode anyway! Your answer is not necessarily a bad one.

If anyone reading this knows an original source for the formula that gives a solid foundation for it, rather than just an ad-hoc linear interpolation, I would love to know.

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