Click on an item below to see the definition
Carryover effect:
This is a problem encountered in crossover trials. Sometimes the influence
of the first treatment carries over when the second treatment is applied. Avoid this by having a long enough washout
period before crossing over. The carryover effect makes crossover trials inappropriate for some disciplines. For
example, you cannot test a teaching method this way, since once something has been learnt, its effect clearly
carries over into the next period of the study.
See also Crossover trials.
Case control study:
In a case control study, two groups are contrasted: the subjects in one
share a characteristic, usually a disease (the cases), and the subjects in the other do not (the control group).
See also Control group.
Categorical data:
Uses numbers or labels with no implied numeric value, e.g. cause of death:
cancer, cardiovascular, respiratory, other. This is unlike ordinal data which has an order or hierarchy.
See also Data types.
Central limit theorem:
All means tend to be normal. For large sample sizes, the sample mean is
normally distributed (or approximately normally distributed) irrespective of the distribution of
the parent population. The larger the sample, the closer the sample mean to normality.
Chi-square statistic:
The test statistic arrived at during the analysis of classification tables
using the chi-square test.
Chi-square test:
A test used with classification tables to determine the influence (if any)
of one factor (rows) on a second factor (columns) by assessing whether there is
a difference in the proportion of an outcome in two or more groups. Examples of
factors might be smoking (yes/no) against lung cancer (yes/no): in other words,
does smoking status lead to a larger risk of lung cancer, or is it irrelevant?
The chi-square test is not for use on continuous data, but specifically for counts.
See also Classification table.
ci:
See Confidence interval.
Classification table:
This is also known as a contingency table. Data is laid out for analysis with the
chi-square or Fisher's exact tests. The rows represent different groups of subjects, and the columns different outcomes.
The data consists of counts of the subjects falling into each cross-classification.
Clinical significance (relevance):
Getting statistical significance is not necessarily the end of the story.
In a clinical trial you would also want to see clinical significance, which is not always present. In clinical terms,
the statistically significant difference may be so small as to be irrelevant. Note that we may see clinical significance in
a sample, but we cannot conclude that this is a real effect in the population unless we also have statistical significance.
Clinical trial:
A well-defined scientific and ethical study of the effects of a particular treatment regime. Almost always, results are
compared against a control group. Clinical trials are subject to very stringent regulation and codes of practice.
Coding:
Sometimes we are interested in a quality rather than a quantity, eg. Good, bad,
indifferent. The best way to get a computer to handle this kind of data is to numerically code the qualities, eg:
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Deteriorated
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-1
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Stayed the same
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0
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Improved
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1
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Alternatively we may wish to classify continuous data within categories, eg. pulse rate could be coded as:
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Less than 21
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1
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Between 21 and 50
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2
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Above 50
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3
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See also categorical data.
Confidence interval (ci):
An upper and lower limit, within which you have a stated level of confidence that the
true mean lies. These are usually quoted as 95% intervals, which are constructed so that
you are 95% confident that the true mean lies between the limits. To be 99% sure, you need
a wider confidence interval. Increased confidence is bought at the cost of precision.
Confirmatory analyses:
Studies conducted specifically to confirm a particular hypothesis.
Confounded:
When the effects of two or more factors cannot be separated, e.g. a study
recruiting old men and young women confounds age and gender effect. A well-designed study should seek to minimise
such effects.
Contingency table:
Also known as a classification table. Data is laid out for analysis with the chi-square
or Fisher's exact tests. The rows represent different groups of subjects, and the columns different outcomes. The data consists
of the subjects falling into each cross-classification.
Continuous data:
Can take any value along a continuum (eg. body temperature: 98.4, 98.46,
99.9999997 are all valid) as opposed to discrete data which can only take integer values (eg. number of children
in a family).
See also Data types.
Control group:
A reference group involved in a study against which the active
group is compared.
Correlation:
Quantifies the extent to which two variables are related to each other.
it is measured in the range of +1 to -1. A correlation of +1 indicates a perfect positive relationship, ie.
as one goes up, the other goes up by the same amount. A correlation of -1 indicates a perfect negative relationship,
ie. as one goes up, the other goes down by the same amount. A correlation of 0 indicates that the two variables
are completely independent of each other.
See also Linefitting.
Count:
Exactly as it sounds, eg. returned tablet count.
See also Data types.
Covariance:
The variation in common between two related variables.
See also Analysis of
covariance.
Covariate:
A secondary variable that influences the outcome of an experiment.
Critical value:
The value at which a decision rule triggers significance.
Crossover trial:
A study that looks at two treatments, where each individual in the study
crosses over from the first treatment to the second: particularly effective because each individual acts as his or
her own control. However, it is not an appropriate technique for all disciplines.
See also Carryover effect.
