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2 So, what are data?

As far as the experimental pharmacologist is concerned, data are simply numbers generated by our experimental observations. In some cases, the answer may be ‘Yes’ or ‘No’, ‘Present’ or ‘Absent’, where the observation has no discernible magnitude, but subsequently we assign value to these observations to allow statistical analysis. In other experiments, the data may simply be recorded as the number of times a particular event has occurred within a certain time period, for example, when a specific behaviour (e.g. head‐shake) is exhibited by rodents in behavioural experiments; such data are known as quantal data. In other cases, data may be described as a continuous variable, such as height or blood pressure. So, data may have different characteristics, and it is important to ascertain the type of data obtained from our observations since this determines the subsequent approach for the Descriptive and Inferential Statistical analysis and the presentation of such data. For the time being, let us just consider how you are going to present your data.

Data handling and presentation

Whatever the type of data our experiment has generated, we need to present our results in some form or other such as text, tables, or figures. The form that is chosen depends on the type of data you have and the message you wish to convey.

Text

Typically, data presented as text in the body of a manuscript (e.g. your laboratory report or that paper you wish to submit to the British Journal of Pharmacology). The following is a description of the phenylephrine data provided in the previous chapter (see Figure 1.3).

Figure 1.3 summarizes the ability of increasing concentrations of the α₁‐adrenoceptor agonists, phenylephrine, to induce contractions of the isolated anococcygeus muscle of male Wistar rats in vitro (N = 70). Data were analysed by Repeated Measures ANOVA. However, initial Mauchly's test indicated that the assumption of sphericity had been violated, χ² (90) = 1903.64, p < 0.001; therefore the degrees of freedom in the subsequent Repeated Measures ANOVA test were corrected using Greenhouse–Geisser estimates of sphericity (ε = 0.156). The Repeated Measures ANOVA revealed significant variation between the contractile responses according to the final organ bath concentration of phenylephrine [F(2.027, 139.862) = 1618.36, p < 0.001]. Post hoc analysis (repeated Paired t‐test with Bonferroni correction) revealed significant differences between successive concentrations in all pairwise comparisons (p < 0.01) except between the highest two concentrations examined (p > 0.05). These data clearly show that the response of the anococcygeus muscle is dependent on the concentration of phenylephrine to which it is exposed. The threshold concentration to significantly induce a response was 2 × 10⁻⁸ M with a maximum response achieved at 4.1 × 10⁻⁵ M. The mean EC₅₀ for phenylephrine was 3.6 (2.8, 4.7) × 10⁻⁷ M, pEC₅₀ = 6.44.

Note that the manuscript description of the data contains the same factual information as the legend for the figure (see Figure 1.3) but allows the opportunity to provide some extra detail, and the language used does not have to be so concise.

Tables

Generally, tables are used by experimental pharmacologists for two principal purposes in reporting experimental data; either as a means of summarizing their own data from a series of experiments or to summarize a substantial collection of data or information from a series of previously published studies already in the public domain.

Table 2.1 summarises the potency of atropine, a muscarinic cholinergic antagonist, and mepyramine, a histamine H₁ receptor antagonist, to block the contractile responses to acetylcholine and histamine in the isolated guinea pig ileum in vitro.

Table 2.1 The relative potency of atropine and mepyramine on muscarinic and histaminic responses in the isolated guinea pig ileum.

	Atropine	Mepyramine
Acetylcholine	9.0	>5.0
Histamine	5.8	9.3

Summary of pA₂ values for atropine and mepyramine for acetylcholine‐ and histamine‐induced contractile responses in isolated guinea pig ileum in vitro. Data on file.

The table summarizes the pA₂ values (−Log₁₀ of the antagonist concentration, expressed in M values, estimated to double the EC₅₀ concentration of the respective agonist); the magnitude of the pA₂ value reflects antagonist potency on the receptor system stimulated by each agonist. The data indicate that atropine is about 1000 × more potent on muscarinic M3 receptor than on histaminic H₁ receptors, while mepyramine may be up to 10 000 × times more selective for H₁ receptors. This table demonstrates the phenomenon of differential antagonism expressed by atropine and mepyramine for these two receptor systems, and the relationships between these values are far easier to see in the table compared with when the values are buried in a paragraph of text.

In contrast, Table 2.2 summarizes the association of neuropathic pain with various disease states in terms of its classification and aetiology according to NICE clinical guidelines. This table simply shows how a wealth of information may be summarized efficiently; to describe all the relevant clinical studies within the body of a manuscript would be laborious and time‐consuming to prepare and, most likely, extremely boring to read.

Table 2.2 Disease associated with neuropathic pain.

Disease	Classification	Aetiology
Painful diabetic neuropathy	Peripheral	Metabolic
Cancer pain due to surgery or chemotherapy	Peripheral	Paraneoplastic
HIV‐related neuropathy	Peripheral	Infection
Post‐herpetic neuralgia	Peripheral	Infection
Radiculopathy (nerve compression)	Peripheral	Trauma
Spinal cord injury	Central	Trauma
Multiple sclerosis	Central	Neurodegeneration
Post‐stroke pain	Central	Neurotoxic
Phantom limb	Peripheral/central	Trauma
Cancer	Peripheral/central	Paraneoplastic

Classification is based on originating lesion. NICE clinical guideline 173 (2013).

Figures

In most cases, experimental data may be most efficiently communicated by the use of figures.

 Line charts are useful to show trends in categories. Care must be taken to ensure that the use of line charts is not confused with scatter plots. While the magnitude of the data (shown on the Y‐axis) may be a continuous variable, the values on the X‐axis in line charts are not, and the data are plotted at set intervals or individual categories along the X‐axis; line charts are therefore wholly inappropriate for plotting information where the X‐axis reflects the magnitude of a continuous variable.

 X‐Y Scatter plots should be used when you wish to show the relationship between the magnitude of two sets of continuous variables. Figure 1.3 is an example of an X‐Y scatter plot, where Log10 of the molar drug concentration is plotted along the X‐axis and the magnitude of the ensuing response (expressed as % maximal response) is plotted up the Y‐axis. In both cases the sets of values may take any value within the range set along each axis.

 Bar charts are typically used to compare values across a few categories. Figure 1.1 is an example of a bar chart where the height of each bar represents the magnitude of the parameter measured (i.e. locomotor activity) according to the category of drug treatment combination administered to the animals used in the study. Consequently, bar charts are very similar to line charts and just convey a different visual impression of the data.

 Histograms are similar to bar charts where the frequency of continuous data (Y‐axis) is plotted against the pre‐defined ranges of the values (X‐axis).

 Box–whisker plots provide a representation of the key features of a univariate sample of data. The whiskers indicate the range while the box indicates the median and upper and lower quartiles.

 Pie charts may be used when you wish to express and compare categories of observations as proportions of a whole. So, if you can set your total to 100%, then each category should reflect a proportion of the total and be expressed as a percentage. I have used pie charts in my explanation of the theory behind Analysis of Variance, where the total size of the chart reflects the total sample variance in the data while the size of the segments reflects the relative sizes of the Between and Within sample variances (see Chapter 15).