Anthony Pym


_Video material

Statistics for Novices 
An introduction for myself and for a few students contemplating research

Anthony Pym 1999 


Research projects in linguistics and cultural studies may or may not need some basic statistics. 

Statistics are NOT needed when: 

1. You have no quantified data (i.e. no numbers) and you are happy doing qualitative research 

2. You have no hypothesis to test (i.e. you’re not really doing research) 

3. You can get all the information you need just by looking at the relative sizes of data (in graphs, by calculating simple averages, counting on fingers and toes or whatever) and you thus do not need to say how strong or weak a relation is. 

Statistics may be needed when: 

1. You want your work to look scientific 

2. You are dealing with a probabilistic hypothesis 

3. You do have to say how strong or weak a relation is, and you can’t tell just by looking at a pie chart, a graph, or some simple representation 

4. You have to say to what extent the relation observed cannot be due to chance (i.e. how significant it is, or better, how representative your sample might be). 


Hypothesis: A proposition that can be proven true or false on the basis of some kind of observation or testing procedure. 

Probability: The likelihood of an event occurring. If an event has never occurred in the field observed, its probability is 0. If it has always occurred, its probability is 1. Probability can thus be expressed as a number between 0 and 1. 

Probabilistic hypothesis: In a very loose sense, a  hypothesis of the kind ‘when X occurs, Y will tend to occur also’, or ‘The more X, the more/less Y’, or ‘when X occurs, Y will tend to occur more than Z’, and so on. 

For example, here is the hypothesis that we’re going to test: ‘The more recent the menu translation, the lower evaluation it will receive from foreigners.’ (i.e. translations of menus in restaurants are getting worse) (Fallada 1998). This does not mean that all recent translations are bad, nor that all old ones are good. It merely posits a tendency, a probabilistic relationship that must be observed, quantified on the basis of a sample, and tested to see if it is significant. 


Variable: A feature that can be quantified. Better, a question that can be answered in a quantitative way. In our example the two variables are ‘date of menu translation’ and ‘evaluation by foreigners’ (i.e. the questions ‘When was the translation done?’, and ‘What score does the user give?’). 

Data: The numbers that quantify the variables; the actual answers to the questions. In this case, the dates of the translations, and the scores given by the foreigners. 

Value: Each of the actual answers (e.g. 1988, 1998, or 30, 50, 60 over 100). 


Nominal data: Data that cannot be related in an ordinal way. For example, the menus are known by the name of the restaurant they come from, but those names are not relevant to our research. So we codify the menus as 1, 2, 3, 4, 5, 6, 7, and so on. These numbers are names; they are nominal data; they cannot be compared in terms of intervals. 

Continuous or interval data: Data that are significant in an ordinal way. When the evaluators give scores to the menus, a score of 70 is worth more than a score of 50, and so on. It is thus significant to compare and measure intervals. 

The difference between nominal and continuous data seems simple enough. As indeed is talk about nominal and continous variables. But the difference is not always quite so clear. For example, in this research the sample included seven menus from the 1960s-1970s and seven from the 1980s-1990s. The dates could have been treated as continuous data but it was considered more convenient to look at the two groups of menus in terms of ‘old’ and ‘new’, just comparing the two groups. This meant that the dates actually became nominal data, naming the two groups and nothing more. 


Mean: What everyone else calls the 'average' of a set of data. An English girl evaluated the 14 menus as follows (scores out of 50): 


    Old 35 45 45 45 30 40 40
    New 20 00 35 10 30 30 15

To get the mean we add up the scores and divide by the number of scores. The mean of the top row (Old) is 40; the mean of the bottom row (New) is 20. So, on average, the older menus were considered better than the newer ones. 

Median:  The middle score, when the scores are put in order. For example, we could order the above scores as follows (it makes no difference, since the order of presentation was only based on nominal values anyway): 



Here the median is 40 for OLD and 20 for NEW. So the medians are in this case the same as the means. 

Sometimes the mean is misleading because of some anomaly in the sample. If, for example, one of the New menus scored a maximum 50, the mean would go up to 22.1 but the median would stay at 20. Medians are thus used as a way of reducing the effect of these exceptional score or ‘outliers’. But it is often just as easy and effective to delete the outliers and proceed with means (as will be explained below). 

Often means are all you need to know about statistics. In the case of the menus, six evaluators were used, in three pairs of two (English-speakers with no Spanish, English-speakers with good Spanish; German speakers with no Spanish). Means were then used for each pair, since the differences between them were not great. The questions concerned ‘Language’ (L) features and ‘Culture’ (C) features, so the mean scores are presented in two blocks, as follows: 



In all cases there is a clear difference between the Old and New menus, so there is little need to keep doing statistics in order to test this particular hypothesis. The mean difference for Language questions was 18.0; the mean difference for Culture questions was 15.6. The overall mean difference was thus 16.8 points. This is great enough to be declared significant without any further ado. 


However, we might want to make sure that this difference is not just due to chance. Further, we might wonder if the difference between the Language and Culture scores was significant, or if it is entirely by chance that the English-speaker who know Spanish gave higher scores and made less of a difference between the Old and the New menus. To answer these questions, we need some kind of statistical test. 


In all these cases we have to consider the possibility that what we think we have found is just due to chance (be it good luck or bad luck). This ‘chance’ possibility is expressed as the ‘null hypothesis’ (H0), which is the hypothesis that we DON’T want to be true. Our null hypotheses would thus be: 
- The mean scores for New and Old menus are exactly the same. 
- Mean scores for Language and Culture radically different (i.e. without correlation). 
- Mean scores for all evaluators are radically different (i.e. without correlation). 


For our findings to be significant, the probability of them occurring has to be greater than that of the corresponding null hypothesis. 

One indication of that probability is how closely the scores are grouped around their mean. If there were a lot of randomness in our data, or if our sample were simply too small for the phenomenon we are trying to test, the scores would wander from high to low for both the Old and New menus, and any difference in the means would be due to no more than luck. We are thus interested in measuring how close the scores are to their means. 

The range is the distance between the highest and the lowest score. On the basis of the above numbers, the range for Old menus is 43.5 - 32.8 = 10.7. The range for the New menus is 27.8 - 15.7 = 12.1. So the scores for the Old menus are more closely grouped than those for the New menus. But this doesn’t really tell us very much. 

If we want to measure the dispersion of all the scores, and not merely the highest and the lowest, we have to measure the standard deviation. This measures how far all scores are away from the mean, whether above or below the mean. 

To do this manually, you take the difference between each score individual score and the mean, then square all those differences (so it doesn’t matter if they’re greater than or less than the mean), then add them up and divide by the number of scores less one. 

If you’re smart, you just feed your scores into a computer programme (I’m using StatView), ask the programme what the standard deviation is, and write down the answer. In this case the standard deviation for Old is 38.8, and for New is 48.4. So the scores for the Old menus are still more closely grouped, and this still doesn’t tell us very much. 


By looking at how well grouped your scores are, and bearing in mind how many scores you have (i.e. the size of your sample), statistics can estimate the probability of those scores representing either normal patterns or simple chance. More technically, it is possible to assess how extreme a sample’s mean is with respect to the distribution of means for all possible samples. 

This makes sense. If you just have two scores and they are closely grouped (say, 40 and 42 in our example), that grouping is not as reliable as data with five scores and a slightly wider range (say, 38, 40, 40, 41, 42). The significance of the patterns we find thus depends on BOTH the grouping of the data AND the number of items in the data. This significance is measured in terms of the probability that the grouping is due to chance. That probability is expressed as the value p (or 'p-value'), which will be a number between 0 and 1 (remembering what we said about probability above). 

To assess significance, you thus either read a book on statistics (here we are drawing on Wright 1997) or feed your data into a computer programme (we will be using StatView and a bit of KaleidaGraph). If you do the latter, you will usually do something called a t-test and just  look for a p-value. If the p-value is very small (usually written as <0.001), the significance of your data is okay (i.e. the probabilityof the null-hypothesis is very low). If the p-value is big (usually 0.05 or more), try something else. 

The p-value for both our groups of menus is < 0.001. So we don’t really have much to worry about. But someone might want to know what is going on. If so, move to the next section. 


T-tests were invented by a man who used the pseudonym ‘Student’. So they are sometimes called Student-tests. But they are not just for students. 

As mentioned, T-tests are used to assess the statistical significance of data. Of the several types of t-test, a paired t-test is used to compare sets of data that are matched in some way and we want to see if the means are different (i.e. if there is some general significant difference between the two sets of scores). 

This could involve comparing two variables for the same people, as in a Before-After study (scores before a lesson vs scores after a lesson). In these cases each of the scores in one group corresponds to a score in the other (i.e. the same subject, before and after) and we are hoping that there will be a significant difference between the two means (i.e. that all the individual subjects will have learnt something from the lesson). The data are thus said to be paired. 

In our menu example the main variables we are interested in are not really paired, since we have decided to treat the dates of the menus as nominal data. 

Further, the paired data that we do have are not really suited to a t-test. Since all the menus were evaluated for Language and Cultural errors, for each case (each menu) we have a Language score and a Culture score. These are indeed paired. But we are not going to hypothesize that the means between the two are significantly different, since there is no change or event separating the two sets of scores. In fact, we would hope that the scores are related in such a way that there is either no significant difference or that when one goes up, the other goes up (i.e. a good menu is good in terms of both Language and Culture errors). 

In these cases we simply test for correlation/covariance, as explained below. No t-test is necessary. 

Perhaps the only part of our example that is suitable for a paired t-test is at the end of the research, where the worst menus (those from the 1990s) were retranslated with the aid of official glossaries for restaurants. These retranslatations were then assessed by one group of informants, with clearly better results. The scores were as follows:


When these scores are put into StatView and a paired t-test is applied, here is what we get: 

This tells us that the difference between the means is 16.429 (the mean for the Before menus is actually 15.714; the mean for the retranslations is 32.143). It also tells us how big the sample is, since the DF here stands for ‘degrees of freedom’ and actually counts the number of cases minus one (thus, 7 menus - 1 = 6 DF). But we can get all that by counting on our fingers. 

(Don’t ask why the number of cases is called ‘degrees of freedom’; don’t ask why we subtract one; this is for idiots, so just read on, okay?) 

Fortunately the test also gives us a number known as a t-value, here 4.223 (the + and - signs don’t matter, since they only depend on what group we list first). Basically, the bigger the t-value, the greater the difference and the happier we should be. But life is not quite that simple. In order for our finding to be significant, the t-value has to be greater than the minimal t-value for the particular degrees of freedom and threshold of significance (sometimes called an a value) we are concerned with. Here we have a DF of 6 and our threshold may as well be the normal 0.05 (i.e. a p-value above this would not enable us to exclude the null hypothesis). 

So, you go to a t-table (Student’s t-distribution), go down the df column (on the left) until you get to 6 (or whatever), go across to the corresponding value in the 0.05 column, and you get a number, in fact a t-value. If we are doing a two-tailed test, that number is 2.45. That means that our own t-value has to be greater than 2.45 if our finding is to be significant. In fact our t-value is 4.223, so our finding is indeed significant, and we have a right to be happy. 

Now, you can more or less forget the previous paragraphs (if you want to know about one-tailed and two-tailed tests, consult a book; if you have to decide and you don’t have a book, choose two-tailed). You can forget most of this because our computer programme also gives us the corresponding p-value. In this case the p-value is 0.0055. As mentioned, to have a significant finding we generally only need a p-value of 0.05 (expressed as ? = 0.05), although this is merely an informal conventional threshold that could go higher or lower as the case may be. In our case here, the p-value is well below 0.05 so our pattern is significant and that’s all we really need to know. 

We can then express this result as follows: 

t(6) = 4.223; p = 0.0055 

And this is exactly what you should put in your paper when you are giving your results. We’ve given the t value (although we are not really interested in looking it up in the tables), we’ve given the df (in brackets), and we have given the all-important p-value. This should impress the multitudes. 


Group t-tests are used when you want to compare the one variable for two groups of cases. This is what happens in our menu example, where we basically want to compare the scores of the Old menus with those of the New. But group t-tests may also be used to compare experimental and control groups. In all these situations we hypothesize that there is a significant difference between the two groups for the variable we are interested in. 

What we are comparing are the means for the two groups, and the significance of whatever patterns we find will increase as the number of cases in the two samples increases. However, here we are not interested in the individual differences between each item and its ‘pair’; here we are only comparing the means for each group taken as a whole. 

To get the degrees of freedom here, we simply add the numbers of cases in the two groups (n1 + n2) and subtract 2. For example, we have 42 assessments of the Old menus (n1 = 42) and the same number for the New menus (n2 = 42), so df = n1 + n2 -2 = 82. 

Once again, our test will give us a t-value that we can compare with the minimum t-value required for a significant result. And the test gives us a p-value, which expresses significance without further ado. 

If we feed all the menu scores into StatView, just listing them in one column and attaching nominal variables in a second column (I used 1 for New and 2 for Old), we then select ‘t-test (unpaired)’, select the first column as the continuous data and the second column as the nominal data, and here is what we get: 

So the mean difference between the scores for the Old and the New menus is 16.905 points (the positive or negative sign only depends on the arbitrary order in which we selected the columns), and this is highly significant because the p-value is very low. 

We would then express this as: 

t(82) = 9.039; p < 0.0001 

And if we want to know what’s going on with the means and standard deviantions for the two groups, it’s all in the descriptive statistics that StatView has given us in the second of the above boxes. 

A further example may be borrowed from Tiina Puurtinen (1997), who was interested in comparing the syntactic constructions in translated vs non-translated children’s literature. 

Puurtinen constructed two corpora, one of Finnish originals, the other of translations from English into Finnish. She then took 10 passages of 2000 words each from both corpora and counted the numbers of nonfinite clauses. The mean numbers of nonfinite clauses were then calculated for the two corpora, and these means were compared using a group t-test. 

When I feed similar values into StatView (putting all the scores in one column, and using the second column for the nominal variables 1 and 2, for Nontranslated and Translated texts respectively), this is what I get: 

This tells us that the mean difference of 5.72 is indeed significant, since the p-value is well below the general threshold of 0.05. In Puurtinen’s own research we find p-values that are indeed lower still (p < 0.01), so what she found was more significant than what I found. 

Group t-tests assume that the two groups have a Normal distribution (like a bell curve) and more or less the same degree of grouping (standard deviation). It follows that when these assumptions are not valid, the results of the test are not particularly valid either. 

This is interesting when we look at the descriptive statistics for the above examples (the numbers given in the second, bigger boxes). In the case of the menus, the standard deviation for the New menus (group 1) is about twice than of the Old menus (group 2), so we might not feel very happy about using a group t-test here (although with p < 0.0001 we perhaps should not worry too much). In the second example, however, the standard deviations of the two groups are very similar, so we would feel the group t-test to be entirely appropriate even despite the slightly higher p-value. 


If we do feel uncomfortable about big differences between the standard deviations of our groups, there is often a simple solution: shoot the numbers we don’t like. 

This means that, if our data show one or a few cases that are clearly very different from the rest, at either the top or the bottom of the range, we can decide that they have  no real reason to be in our sample, that they got there by accident, that we are not very interested in them. And then we eliminate them from our data. 

In the case of the menus, the New group has a bid standard deviation because two menus that were so badly translated as to be laughable. If these two menus are treated as outliers and eliminated, the standard deviations become closer and our group t-test seems a little more justified. Further, the difference in the means of the two groups remains highly significant: 

The elimination of these outliers has the advantage of convincing us that our result is not merely due to some accident in the sampling process. 

Of course, we might also be genuinely interested in the outliers, if only from a qualitative point of view. The high standard deviation for the New group, with or without outliers, is of interest for any hypothesis that would associate recent developments of the translation market with relatively erratic, uncontrolled performance and with a decline in collective professionalism. This was indeed one of the qualitative findings of the research. 


T-tests are not saying that a positive relation exists. They are merely expressing the degree of certainty with which the null-hypothesis (what we don’t want to find) can be rejected. 

In the menu example, p < 0.0001 thus means that there is very little probability that the difference between the means of the two groups is due to chance. We have not proved that all menus produced in the 1990s are worse than all the menus produced in the 1970s; we have not shown any causal relation between the two variables involved; all we have done is assess the probability that the mean differences between our samples are due to chance. 

If you want to say more than that, you need more than these statistics. 


T-tests are used when we hypothesize a patterned difference between two variables or between two groups. 

However, if our hypothesis is that there is NO significant difference between two variables, we are perhaps better off doing a simple test of correlation. 

This is the case, for example, of the scores for the Language and Culture errors in the menus. Here we are interested in the possibility that a high Language score corresponds to a high Culture score for the same menu. In other words, when the value for one variable moves up or down, we would like the value for the other variable to move up or down accordingly. 

This moving up and down together is actually called covariance, which can be measured as such. However the numbers given for covariance analysis depend on the units used in the measurement (measurements in Fahrenheit and Celsius will give different covariance values). It is easier and more meaningful to go straight to a correlation analysis. 

To get the correlation, put the scores into StatView and see what it says. 

Here, for example, are the Language and Culture scores for seven menus: 


We want to know if there is a good or bad correlation between these variables. When we select the ‘correlation matrix’ test, here is what we get: 

Absolute direct correlation is +1.0 (whenever one side goes up, so does the other and to a corresponding degree); an absolute lack of linear correlation would be 0.0 (no relation between the up and down movements on either side); an absolute inverse correlation would be -1.0 (whenever one side goes up, the other goes down and to a corresponding degree). So here we find the Language scores correlate absolutely with the Language scores (which should be no mystery!) and that the degree of correlation between the Language and Culture scores is 0.891, which is high and thus a good indication of the relation we were hypothesizing. 

Correlation matrixes can be done with more than two groups. So it is a quick and easy way of seeing which variables move together. 


If the correlation value 0.891 doesn’t mean much to us, we can also visualize what is happening by drawing what is called a ‘scattergram’ or a ‘scatterplot’. This means that one variable goes on the x axis and the other on the y axis (it doesn’t much matter which is where) and our scores are then ‘scattered’ in accordance with these two dimensions. For the above data on Language and Culture scores for seven menus, this is what we get: 

Each of these points represents a menu, located so that we can read off its score for Language (on the y axis to the left) and for Culture (on the x axis at the bottom). 

Clearly, the higher the score for Language, the higher the score for Culture. Which just means that there is a good correlation, as we already know. 

However, we can go a bit further and ask StatView to draw a Bivariate Regression Plot (in the Graph menu, under Analyze). And here is what it gives us: 

As you can see, this is the basic scattergram plus a straight line drawn through the points to indicate the best fit we could hope for. This line is called the regression line. 

Regression lines of this kind are useful when we are trying to predict values for data that we don’t have but could be assumed to lie within the range of those we do have. For example, we might be interested in predicting the Language score for a menu with a Culture score of 32. By just looking at the graph, we go from 32 on the x axis up to the line, mark the point, and then go across to the corresponding Language score, which would be about 39. You also get a formula to do this: 

Language = 10.185 + 0.907 * Culture 

So, if we want to predict the Language score for a Culture score of 32: 

Language = 10.185 + 0.907 * 32 = 39.209 

The numbers under the scattergram also include the R2 (r-squared) value, which measures the amount of shared variance between the variables, i.e. how much of the variance in x is accounted for by y, or vice versa. This is in fact a measure of how well the linear model fits the data. Here we are being told that an estimated (^ means ‘estimated’) 79.4% of the variance is accounted for by the data. Which is quite good. 

This kind of analysis is useful in cases where there is obviously a lot of data missing but we still want to predict general relations as far as possible. 

For example, we might test the hypothesis that the more publications there are in a language, the less the percentage of translations in that language (i.e. big languages translate proportionately less than small languages). The problem here is that there are a great deal of languages in the world but comparable data are only available for about 20 of them (from Unesco). So we can’t really do any sampling; we just have to assess the possible correlation on the basis of the numbers available. When we draw a bivariate regression for these two variables, this is what we get (now from KaleidaGraph, because it’s prettier and we can actually name the languages on the graph): 

This has been taken from Pym (1999). Here is the same thing from StatView: 

Since StatView gives us the formula for the line, we could now predict the translation rate for a language with a given number of publications. For example, we know that Catalan had about 2000 books published in 1980, so its x-value is about 2, but there are no reliable data on the translation rate at that time. We may now try to estimate that rate as the following y-value: 
y = 18.29 - 0.114 x 2 = 18.062 

So we would predict that Catalan had a translation rate of about 18% for 1980. How good is the prediction? Well, the only figure I do have is an estimate of 16.5% for 1977, which is at least close to our prediction. (The linear analysis of these data is actually more useful as a check on people who argue that the English language actively excludes translations, when its low rate could be due to no more than its high number of publications.) 

The R2 here is 0.465, which means that only about 46% of the variance is accounted for by the data. This is a little below the 50% that might make us feel confident. 

Like t-tests, this kind of test assumes that the data have about the same variance in the two groups. In our example this is a risky assumption, since the standard deviation for the Books variable is about 40, and that of the %Translations variable is around 8. This sort of difference suggests that the relation is in fact far from linear.  


Fallada, Carmina. 1998. ‘Are Menu Translations Getting Worse? Problems from the Empirical Analysis of Restaurant Menus in English in the Tarragona Area’. 

Puurtinen, Tiina. 1997. ‘Syntactic Norms in Finnish Children’s Literature’. Target 9:2. 321-334. 

Pym, Anthony. 1999. ‘Two principles, one probable paradox and a humble suggestion, all concerning translation rates into various languages, particularly English'

Wright, Daniel B. 1997. Understanding Statistics. An Introduction for the Social Sciences. London, Thousand Oaks, New Delhi: Sage. 


Last update 13 January 2000  

© Anthony Pym 2014
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