What is normal?

A couple of months ago I wrote a post entitled normative data capture Part 1. No-one has yet demanded Part II but I’ll give it anyway. The earlier post concentrated on what sort of numbers were required to determine normative ranges for any data assuming we want a reasonable estimate of both the average and the standard deviation.

Once we’ve got the numbers sorted out then the question arises “What is normal?” It might be worth starting with a paragraph addressing the political dimension here. The word “normal” is considered inappropriate in some circles because of the connotation that anyone else, our patient for example, is “abnormal” which is considered a negative term. The response from some researchers (particularly Americans?) working with children has thus been to prefer the term typically developing. This presumable implies that our patients our atypical and I’m not sure that that is any better or worse than abnormal.  What I do appreciate is that we are all abnormal in some regard. The question should not be whether the person is normal or abnormal but whether their gait pattern is. I think normative stresses this emphasis that it is the data or the pattern that is abnormal rather than the individual (but others may think differently).

But then what is a normal gait pattern?  In my dictionary there are various definitions of normal and the closest to the sense in which we are using it is not deviating from the standard. Even this though is not particularly close. I suspect that what we really mean is representative of the population. This raises the questions of how we consider people, with conditions such as  cerebral palsy  in relation to this population?  I think that conceptually they should be considered as part of the population. Thus the normal population includes people with cerebral palsy (and other gait disorders). In childhood and early adulthood at least, these conditions are quite rare (approximately 1 in 500 people is born with CP, one of the more common conditions affecting walking) so true normative ranges (calculated over a wide enough sample) would be very little affected by including or excluding them.

Of course we most often collect normative data from much smaller samples (my previous post suggested that 30 might be regarded as a reasonable number). In this case it makes sense to specifically exclude people with obvious neuromusculoskeletal pathology not because we regard them as abnormal in principle but because the statistics of the situation dictate that the normative data that we obtain by excluding them will be closer to normative data for the entire population than the data we would obtain if they were included. (Including one person with CP in a sample of 30 runs the risk of obtaining normative data that is quite different from that which would result from the one person in 500 in the general population)

A more common problem is a number of anatomical and physiological characteristics which have a wide range within the general population such as in and out-toeing, tibial torsion, femoral anteversion, flat-feet and high arches. Some health professionals will want to define an arbitrary and often subjective cut-off beyond which the individual is labelled as having an impairment and exclude them from the normative dataset. I remember hearing one story of a gait analysis service that was interested in providing normative data for a foot model and simply collected a group of individuals who, to them, had no obvious neuromusculoskeletal impairment and were entirely asymptomatic. The team was later joined by another health professional who looked at the dataset and concluded that quite a large proportion of the cohort had either flat feet or raised arches and wanted these abnormal people deleted from the dataset.

This of course raises the prospect of self-fulfilling prophecy. People with flat feet are considered as abnormal because they have data that falls outside the range of those who have been assessed as not having flat feet. This is clearly daft. Normative data ranges should ideally be generated with randomly sampled datasets from the whole population. In practical situations random sampling is extremely rare but it is inappropriate to select participants on the basis of some pre-supposition of what normal is (unless the abnormality is so rare and severe that the inclusion in a small sample risks skewing the data as described above).

There is another problem when we start to look at older populations. Iezzoni et al. (2001) suggest that over 10% of the entire population have some difficulty walking as little as 400m, rising to nearly 50% if we look at the population aged 80 and over. There is considerably potential for inclusion or exclusion of these individuals to affect normative data. If we are concerned with what data to use for comparative purposes in older populations, however, then I think the goal posts have shifted.  What we really require is not normative reference data but reference data from healthy people within a particular age range or more specifically those without any specific neuromusculoskeletal pathology. If we want a convenient shorthand then perhaps we should refer to a healthy gait pattern in these circumstances rather than a normal one.

There are the same risks here of subjective decisions as to where the healthy range ends and pathology starts which are largely unavoidable. These can be addressed to a certain extent by defining explicit and objective inclusion criteria. We might not agree with definitions but at least we will know what they are. Even these are problematic however because it is very easy to introduce sampling bias when recruiting. When selecting healthy controls for a study there will be a tendency to select the healthiest available.  All will fulfil the inclusion criteria but they may not be representative of the population of all people who fulfil those criteria.  The solution here may be to specify the characteristics of the sample that were actually recruited rather than the inclusion criteria for the study.


Iezzoni, L. I., McCarthy, E. P., Davis, R. B., & Siebens, H. (2001). Mobility difficulties are not only a problem of old age. Journal of General Internal Medicine, 16(4), 235-243.



  1. It would be interesting if there was a resource providing a detailed breakdown of the possible factors that do influence our gait pattern. You mention age and pathology as factors: it would interesting to know the relative importance of things like gender, culture, anthropometrics, walking speed – even mood and time of day. If someone could somehow nail down the scope and relative importance of all the different factors, then you could be confident in considering which ones to take into account and which one to ignore when defining your population characteristics \ inclusion criteria.

    I’m also not always sure what normative reference data is actually telling us. What does the fact that a gait pattern is ‘abnormal’ have to do with it being functionally deficient? Is there a causative link? I would have thought that the brain optimizes the gait pattern using some functional (energy efficiency?) or subjective (comfort?) criteria, no matter how it gets there in terms of joint motion. This makes me think of the central governor theory of the brain, which I understand to mean that the brain regulates our physical performance to well below what we are capable of except in emergencies. This suggests to me that because walking is so ‘easy’ for healthy walkers, their body has a lot of excess capacity for achieving its functional criteria in a multitude of different ways (depending on how they are feeling physically and emotionally). On other hand, perhaps a child with CP has fewer gait patterns available to them with which to achieve adequate step length, speed etc. So different level of exertion in walking (even with identical TSPs for example) could influence group variability.

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