In the case of percentage change, the change should be calculated as 100 × [(final – original)/(original)] values. Most of the time, we wish to summarize a set of measurements and also report a comparison. Let’s look at our jumping frogs [3]. We reported the mean distances that these groups jumped, to the nearest check details millimetre. Perhaps that was a little optimistic, as at the competition the jump length was measured to the nearest ¼ inch! How shall we summarize the results of that first test on trained and untrained frogs? To describe the first sample we studied (Figure 1) we need

to express two different concepts to characterize the samples. We give a measure of central tendency to summarize the magnitude of the variable (examples would be the mean or median values) and a measure of dispersion or variability (such as a standard deviation or quartiles). In the case of our frogs, the jump lengths and variation of the jump lengths are the important features of our sample. These distances were normally distributed (not a common feature in most biological experiments) so to describe the overall performance of the untrained frogs we report the sample mean distance jumped. To describe the variability or spread,

we report the sample standard deviation. The final value needed to characterize the sample is the number of measurements. So, in the case of the untrained frogs, we report that the distance jumped was 4912 (473) mm, AZD1152-HQPA purchase where these values are mean (SD), and we could add (n = 20) if we have not already stated the size of the sample used. These values summarize our estimate of the population characteristics. There is no added benefit in using

the symbol ± and reporting 4912 ± 473 mm. In fact the use of this convention can be confusing: is it the mean ± SD, the mean ± SEM (defined shortly) or a confidence Calpain interval? Many authors choose to use the standard error of the mean (SEM) as a measure of the variability when describing samples. This is incorrect: this value should only be used to indicate the precision with which the mean value has been estimated. As we saw in our frog studies, this value depends very much on the sample size. We told our readers how many frogs we sampled, which is how we achieve the precision. Note that care should be taken when interpreting the SEM as it stands. Here, it is the standard deviation of the sampling distribution of the mean. It tells us how the sample mean varies if repeated samples of the same type (with same sample size) were collected and the mean calculated. In fact 68% of these estimated means would be expected to lie in a range between one SEM less and one SEM greater than the actual population mean. Thus there remain a substantial proportion of sample means that do not fall within this range. To assess how precisely a sample mean has been characterized, the preferred measure of precision of a mean estimate is the 95% confidence interval.