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Glossary
Glossary of Statistical Terms
Age
Standardised
Rate:
Age standardisation is a method used to adjust the crude rate (see below) to remove the effects of
different age structures when comparing populations. There are two methods of age standardisation: Direct
and Indirect.
Directly Age Standardised Rate: Direct standardisation is used when the study population is large and
the age-specific rates are reliable. The directly standardised rate is the overall rate that would have
prevailed in a standard population if it had experienced, at each age, the death rate of the study population.
Indirectly Age Standardised Rate: Indirect standardisation is used when the study population is small
and the age-specific rates are unreliable or not known. Indirect standardisation is an adjustment to account
for variation between actual numbers in the study population and the numbers which would have occurred
if the study population had experienced the age-specific rates of a standard population.
Confidence
Interval:
The confidence interval around a sample estimate is a method used to indicate its reliability. Relatively
wide confidence intervals indicate poor reliability, usually as a result of small sample sizes. Confidence
intervals are calculated from the sample data and expressed as a range of values (upper and lower) at a
confidence level chosen by the researcher (usually set at 95%). A 95% confidence interval for a sample
estimate is an upper and lower range that has 95% probability of bracketing the true value of the
parameter. In other words, if 100 samples of the same population were taken, 95 should produce a
confidence interval that contains the true value of the parameter being estimated.
Confidence intervals are also related to statistical significance testing (see below). Generally, if
independent groups within the same population have confidence intervals that don't overlap, then the
difference can be said to be statistically significantly different. However, the converse isn't always true.
Independent groups from the same population with overlapping confidence intervals may still be
statistically significantly different.
Crude Rate:
The number of observations divided by the relevant population (and then usually multiplied by 1000 or
another appropriate multiplier for readability). For example, total deaths from all causes divided by the total
population then multiplied by 1000 gives the crude death rate per 1000 population.
When interpreting crude rates, it should be noted that rates may be affected by difference in the population
structure for local areas. For example, in an area which is characterised by an older population, this alone
would result in a higher crude death rate for many causes. To adjust for this, age standardisation is applied
(see above).
Incidence:
Refers to the number of new cases of a disease which arises within a population within a specific period of
time.
Mean:
The arithmetic mean, usually simply referred to as the average, is a measure of central tendency of a
collection of numbers. It is calculated from as the sum of the observations divided by the number of
observations.
Median:
Along with the mean, the median is another commonly used measure of central tendency. The median is
the midpoint of a distribution, the number at which half the observations are smaller and the other half are
larger. The median is less susceptible to the influence of a particularly large or small observations (outliers)
in the dataset.
Prevalence:
The number of cases of disease which exist in a population at a given point in time.
Quintile:
One fifth (20%) of an ordered population of observations. For example, 40% of the population lie below,
and 60% above, the second quintile.
Statistical
Significance:
Estimates generated from sample data come with a margin of error, but if an observed difference between
two sample estimates (e.g. rates for metropolitan Adelaide vs. Country SA) is large enough, then that
difference is said to be statistically significantly different. Exactly how large the difference needs to be
depends on various factors, including sample size, but can be determined with a statistical test which
calculates the probability of the observed difference being due to chance alone. If this probability is small
enough, generally less than 5%, then it can be concluded that some effect other than sampling error (e.g.
residential status of the population) is resulting in the observed difference.
It is important to note that when used in statistics, the word significant does not mean important or
meaningful, as it does in everyday speech. An observed difference can still be of practical significance,
without being statistically significantly different.
WORKING DRAFT FOR DISCUSSION (MAY 2013) – HPC State of Our Health
Glossary of Statistical Terms
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