Random error

An important consideration when sampling from a population is that of random error, which results from chance variation in the members of any sample taken from a larger population. Random error may affect the conclusions you draw from a study by affecting the precision of a descriptive study, or the power of an analytic study.

Precision

The precision of an estimate is a measure of the 'repeatability' of this estimate if the same study was conducted again. Therefore, it is a measure of the random error inherent in a sample, which in the case of descriptive studies is closely associated with the confidence interval.

Confidence intervals

Confidence intervals are commonly used in both descriptive studies and in analytic studies in order to indicate the precision of an estimate (whether it be a point prevalence estimate, a mean weight measurement or an odds ratio). Commonly a 95% confidence interval is used - this, simply put, quantifies the range of values which the investigator can be confident contains the true source population value. Therefore, an investigator would have greater confidence that a 99% confidence interval contains the true value than a 95% confidence interval. However, the correct interpretation of a confidence interval can be confusing, relating as it does to a hypothetical situation of repeated sampling.

These issues can be better explained using a hypothetical example. Assuming a study is conducted to investigate the seroprevalence of Peste De Petits Ruminants virus in sheep in one region of an African country. A census of all animals could be conducted, which would allow the determination of the exact seroprevalence (assuming a perfect diagnostic test) - however, this is not logistically or financially viable, and therefore a sample of the sheep population is taken. We will assume that there is no bias at all in the sample, and that a simple random sampling protocol is used. The sample taken gives a point seroprevalence estimate of 30%, and the 95% confidence interval ranges from 20% to 40%. As such, we can be 95% confident that the true seroprevalence to PPRV in this region of the country is between 20% and 40% - no particular seroprevalence estimate within this range is any more or less likely than any other one. Despite this, there remains a small chance that the true seroprevalence lies outside this range. To explain this further, imagine we take another sample from the population, and we get another confidence interval, and we repeat this process again and again until we have 20 individual samples and associated confidence intervals (all from the same source population). On average, we would expect 19 of these (=95%) to contain the true seroprevalence, and 1 of these to not. Note that we cannot say anything about the probability of what the true prevalence is for any of these confidence intervals, since it is either correct (probability of containing the true seroprevalence=100%) or incorrect (probability of containing the true seroprevalence=0%).

Hypothesis testing and study power

Approach to hypothesis testing

Hypothesis testing is commonly used in analytic epidemiological studies, and provides a systematic method of analysing data in order to draw conclusions about the population(s) from which these data were drawn. A common example is when samples are taken from two different populations and a test is performed in order to assess whether some outcome of interest (such as prevalence of infection) differs between the two groups. As it is not possible to definitively state whether or not there is a difference between these two groups (since not all members of the groups are sampled), statistical methods are used to assess the strength of evidence in favour or against a difference.

The approach to hypothesis testing first requires making the assumption that there is no difference between the two groups (which is known as the null hypothesis). Statistical methods are then employed in order to evaluate the probability that the observed data would be seen if the null hypothesis was correct (known as the p-value). Based on the resultant p-value, a decision can be made as to whether the support for the null hypothesis is sufficiently low so as to give evidence against it being correct. It is important to note, however, that the null hypothesis can never be completely disproved based on a sample - only evidence can be gained in support or against it. However, based on this evidence, investigators will often come to a conclusion that the null hypothesis is either 'accepted' or 'rejected'.

In any hypothesis test, there is a risk that the incorrect conclusion is made - which will either take the form of a type I or a type II error, as described below. Note that no single hypothesis test can be affected by both type I and type II errors, as they are each based on different assumptions regarding the source population. However, as the true state of the source population will not be known, both types of errors should be considered when interpreting a hypothesis test (and when calculating the required sample size).

Type I error

This type of error refers to the situation where it is concluded that a difference between the two groups exists, when in fact it does not. The probability of a type I error is often denoted with the symbol α. As this type of error is based on a situation in which the 'null hypothesis' is correct, it is associated with the p-value given in a hypothesis test, which is often set at 0.05 to indicate 'significance'. This means that there is a 5% chance of a type I error (which in the case of hypothesis testing, is interpreted as 'if the null hypothesis was correct, we would expect to see this difference or greater only 5% of the time - meaning that there is [weak] evidence against the null hypothesis being correct).

Type II error

This type of error refers to the situation where it is concluded that no difference between two groups exists, when in fact it does. The probability of a type II error is often denoted with the symbol β. As the 'power' of a study is defined as the probability of detecting the difference when it does exist, it can be calculated as (1-β).