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Glossary

This glossary was published in the Evidence-based Dentistry, 2003;4.1. We thank the editor Dr Derek Richards for giving us permission to present the glossary in this database.

 

Alpha and Beta errors

Alpha (Type 1) errors are 'false positives': that is, the results suggests that a treatment works, when in fact it does not work. Beta (Type II) errors are 'false negatives': that is, the results suggest a treatment does not work, when in fact it actually does.

Association

A known link, or statistical dependence, between two or more conditions or variables: eg, statistics demonstrate that there is an association between smoking and lung cancer.

Bias

Something that introduces a difference or trend that distorts (or could distort) results of a study.

Case-control study

Compares people with a disease or condition ('cases') to another group of people from the same population who don't have that disease or condition ('control's'). A case-control study can identify risks and trends, and suggest some possible causes for disease, or for particular outcomes. 

Cochrane Collaboration

The Cochrane Collaboration is an international effort by researchers, practitioners and consumers to sift through research on the effects of health care. The Collaboration prepares, maintains and disseminates systematic reviews of the effects of health care. The Collaboration prepares, maintains and disseminates systematic reviews of the effects of health care. The reviews are published in the Cochrane Database of Systematic Reviews - one of the components of a regular, electronic publication called The Cochrane Library.

Cohort (study)

A 'cohort' is a group of people clearly identified: a cohort study follows that group over time and reports on what happens to them. A cohort study is an observational study and it can be prospective or retrospective.

Confidence interval (CI)

Confidence interval is the range within which the true size of effect (never exactly known) lies with a given degree of assurance. People often speak of a "95% confidence interval" (or "95% confidence limits"). This is the interval which includes the true value in 95% of cases.

Cross-over trial

A trial where each of the groups will receive each of the treatments, but in a randomised order: that is, they will start off in one arm of the trial, but will deliberately 'cross over' to the other arm(s) in turn.

Cross sectional study

Also called prevalence study: an observational study. It is like taking a snapshot of a group of people at one point in time and seeing the prevalence of diseases, etc, in that population.

Effectiveness (Clinical Effectiveness)

The extent to which an intervention does people more good than harm. An effective treatment or intervention is effective in real life circumstances, not just an ideal situation.

Efficacy

The extent to which an intervention improves the outcome for people under ideal circumstances. Testing efficacy means finding out whether something is capable of causing an effect at all. 

Heterogeneous (geneity)

The opposite of homogeneous. If a set of studies on the same subject have varied or conflicting results, the results of the group of studies are heterogeneous. Examining and explaining this heterogeneity is an important part of reviewing the research on a particular subject. 

Incidence

The number of occurrences of something in a population over a particular period of time: eg, the number of cases of a disease in a country over one year.

Intention to treat analysis

Analysing the results according to the intended treatment to which someone was allocated in a randomised controlled trial (as opposed to the treatment they actually received in the end).

Meta-analysis

Meta-analysis is a statistical technique which summarises the results of several studies into a single estimate, giving more weight to results from larger studies.

Number needed to treat (NNT)

One measure of a treatment's clinical effectiveness. It is the number of people you would need to treat with a specific intervention (eg aspirin for people having a heart attack) to see one occurrence of a specific outcome (eg prevention of death).

Odds

A term little used outside gambling and statistics. It is defined as the ratio of the probability of an event happening, to that of its not happening. Think of it as meaning 'risk'.

Odds ratio (OR)

One measure of a treatment's clinical effectiveness. If it is equal to 1, then the effects of the treatment are no different from those of the control treatment. If the OR is greater (or less) than 1, then the effects of the treatment are more (or less) than those of the control treatment. Note that the effects being measure may be adverse (eg death, disability) or desirable (eg stopping smoking). 

P value

The findings of a study may be just an unusual fluke. Calculating the p value can determine whether or not the results of the study are likely to be a fluke or not. The p (probability) value shows whether or not the result could have been caused by chance. If the p value is less than 0.05, then the result is not due to chance. A result with a p value of less than 0.05 is statistically significant. The 0.05 level is equal to odds of 19 to 1 (or a 1 in 20 chance). (See also confidence interval, power, and probability).

Power (Statistical power)

A study needs to have a specific level of 'power' in order to be able to reliably detect a difference that a treatment might cause. The study needs to have enough participants, who experience enough of the outcomes in question, to be able to come up with statistically significant results.

Prevalence

The proportion of a population having a particular condition or characteristic: eg the percentage of people in a city with a particular disease, or who smoke.

Probability

Probability is the 'chance' or 'risk' of something happening. (It is the word from which springs the 'p' in the notion 'p value'.)

Quasi-random

Methods of allocating people to a trial which are not strictly random, eg allocation by the person's date of birth, the day of the week, by medical record number, or just allocating every alternate person. Quasi-random allocation may look random, but it is not because the group to which a person will be allocated is predictable, and people can start to manipulate who goes in, eg if someone wants to be in the experimental group, but not the control group, they can be placed in the experimental group if their number has come up, and simply excluded from the trial if it doesn't. One, other or both arms of the trial can then be biased. (See also randomised controlled trial).

Randomised controlled trial (RCT)

RCT is a trial in which subjects are randomly assigned to two groups: one (the experimental group) receiving the intervention that is being tested, and the other (the comparison group or controls) receiving an alternative treatment. The two groups are then followed up to see if any differences between them result. This helps people assess the effectiveness of the intervention.

Relative risk (RR)

Also called the 'risk ratio'. It is a common way of estimating the risk of experiencing a particular effect or result. A RR > l means a person is estimated to be at an increased risk, while a RR < 1 means a person is apparently at decreased risk. A RR of 1.0 means there is no apparent effect on risk at all. eg If the RR=4.0, the result is about 4 times as likely to happen, and 0.4 means it is 4 times less likely to happen. (See also confidence interval, odds ratio).

Risk difference

Also called absolute risk reduction. It is literally the difference in size of risk between two groups. eg If one group has a 15% incidence of a disease, and the other has a 10% incidence of the disease, the risk difference is 5%.

Standard deviation

A set measure of how far things vary from the average result (the mean). The mean is the central (average) measure. The standard deviation is a way of describing how far away from this centre, or average, the values spread. eg A mean waiting time in a hospital emergency room might be two hours, but to cover most people's waiting time, you might have to give or take an hour; the waiting time is therefore 2 hours+1 hour. That extra one hour is the standard deviation. A person who waited 4 hours to be seen would therefore be 2 standard deviations from the mean. 

Statistical significance

The findings of a study may be just an unusual fluke. A statistical test can determine whether or not the results of the study are likely to be a fluke or not. That test calculates the probability of the result being caused by chance: it provides a p value (probability). If the p value is less than 0.05, then the result is not due to chance. A result with a p value of less than 0.05 is statistically significant. The 0.05 level is equal to odds of 19 to 1 (or a 1 in 20 chance). (See also p value, confidence interval, power, and probability).

Systematic review

A review in which evidence on a topic has been systematically identified, appraised and summarised according to predetermined criteria (Some people call this an 'overview').

Validity

The soundness or rigour of a study. A study is valid if the way it is designed and carried out means that the results are unbiased - that is, it gives you a 'true' estimate of clinical effectiveness.

Last updated by Marie Nordström