## Chi-square statistic and test

The draft below is part of a study pack I am producing for level 3 students following a B/TEC Maths and Stats unit.

A theory predicts a certain outcome for an experiment – usually found by multiplying list of the probabilities of various outcomes by a sample size. We shall call these predictions the ‘expected values’. You then run the experiment and you are not surprised to find that the observed values – one result for each expected value – are slightly different when compared with the corresponding expected value. But the crucial question is ‘how different’ and then perhaps ‘are the observed values sufficiently different from the expected values to make me disbelieve the theory I used to calculate the expected results’?

In order to answer the first question, we need to find a way of calculating a number that will tell us ‘how far’ or ‘how different’ the expected values are from your observed values. Such a number or ‘statistic’ has been invented, discovered or devised: the chi-squared statistic. The recipe for calculating the chi-squared statistic follows…

Step 1: calculate a list of your expected values based on the theory that you are trying to test. Your list may contain as few as two values or may contain many entries, possibly organised as rows and columns, it all depends on your theory.

Step 2: perform your experiment and list your observed values in a way that facilitates comparison with your observed values (a table springs to mind). Bear in mind that your observed values (and expected values) must be absolute values and not percentages, proportions or fractions. If you are calculating expected counts or frequencies, don’t round off your expected values to the nearest whole one – leave them to a sensible number of decimal places.

Step 3: For each pair of expected and the corresponding observed values, find the difference. Ignore the sign of the difference

Step 4: Square each of the differences found in the last step

Step 5: Divide each squared difference by the corresponding expected value

Step 6: Find the total of all these squared divided differences.

The number you are left with will be positive and may be large (i.e. 10 or 20) or small (i.e. 0.07). This total score is called the chi-square statistic.

Our second question can be summarised as ‘is the chi-squared statistic so large that I can’t believe that the theory describes the experimental situation accurately’?. The answer to this question involves adopting a probability – we say that we will believe that the expected values are not consistent with the observed values if the probability that they are really the same and that random variations explain the difference is less than 5% (or 1%, or 0.5% depending on the level of ‘false negatives’ we are content to accept). We then use a set of chi-square tables to look up a critical value of the chi-squared statistic for the 5% (or 1% or 0.5%) probability level. If the calculated value of the chi-squared statistic is higher than the critical value for the chosen probability level, then we cannot assume that the theory adequately describes the results of the experiment. If the calculated chi-squared statistic is less than the critical value we found from the tables, then we can assume that the theory is consistent with the observed values. When the two values for the chi-squared statistic are close, then we have a judgement call.

There is a catch – in order to find the critical value for the chi-squared statistic, we have to decide on a probability level and we have to know the ‘degrees of freedom’ available in the data. We then enter the chi-square table at the column corresponding to our chosen probability level and at the row corresponding to the appropriate number of degrees of freedom.

The procedure used to calculate the number of degrees of freedom appropriate to a set of data depends on the number of rows and columns in the data. In the case of a simple one column list, we just take one less than the number of items in the list as the degree of freedom for that data.

In the case of a number of rows and columns, you take one less than the number of rows, and then take one less than the number of columns, and then multiply the two numbers. In this way a block of observed values with 3 columns of 10 numbers will have 18 degrees of freedom.

A special case will be of primary interest to us: that of a table of results that contains just two numbers. In this case, there is one degree of freedom.

The concept of degrees of freedom is easy to relate to the experiment involving fruit flies that we will turn to shortly. If you have exactly 60 flies (and counting flies that can move and still become airborne is a skill in itself), and you know that 14 of those flies have vestigial wing forms, then you also know that 46 of the flies have fully formed wings. Once you have specified one number, the other is known by subtraction from the total. There is effectively only one random number in the experiment, hence one degree of freedom.

There is one more correction that is applied in the special case of a set of results with just two numbers – the case that is of primary interest to us. Yates argued that with only one column and two numbers, the differences between the expected and observed results will always be the same but with opposite sign. He went on to argue that this ‘heads and tails’ property of the differences would lead to a ‘lumpy’ chi-square value, and he proposed a ‘continuity correction’ that would smooth out the ‘lumpiness’.

There is also a limitation: the expected values in each cell of your table must be greater than about 5, as we are assuming a normal distribution of differences about the expected value. For frequencies less than a minimum of 5 in a given cell, the chance of a negative deviation is different to the chance of a positive deviation of similar size and the distribution of deviations can no longer be assumed to be symmetrical.