Chapter 5: Key Terms

Causal Association
X does cause Y.

Contingency Table
A type of table that tabulates data according to two dimensions. The column and row totals are known as marginal totals.

Noncausal Association
X does not cause Y.

Scatter Plot
A method for graphically displaying relationships between variables; uses two quantitative variables.

Statistical Independence
When there is no association between two variables.

Criteria of Causality
-Biological Gradient

Biological Gradient
Also known as a dose-response curve, which shows a linear trend in the association between exposure and disease.

A consistent association is one that has been observed repeatedly by different persons, in different places, circumstances, and times.

A specific association is one that is constrained to a particular disease-exposure relationship. In a specific association, a given disease results from a given exposure and not from other types of exposures. One-to-one causation is unusual because many diseases have more than one causal factor.

Strong associations give support to a causal relationship between factor and disease.

We must observe the cause before the effect.

Requires that an association must be biologically plausible from the standpoint of contemporary biological knowledge.

Suggests that the cause-and-effect interpretation of our data should not seriously conflict with the generally known facts of natural history and biology of the disease.

Relates to the correspondence between known associations and one that is being evaluated for causality.

Clinical Significance
Q; pg. 101

Confidence Intervals
A computed interval of values that, with a given probability, is said to contain the true value of he population parameter; a measure of uncertainty about a parameter estimate.

Point Estimates
A single value chosen to represent the population parameter.

The ability of a study to demonstrate an association if one exists.

Statistical Significance
The assertion that the observed association is not likely to have occurred as a result of chance.

Any quantity that varies . Any attribute, phenomenon, or event that can have different values.

A linkage between or among variables; variables that are associated with one another can be positively or negatively related.

Positive Association
As the value of one variable increases so does the value of the other variable.

Negative/Inverse Association
When the value of one variable increases, the value of the other variable decreases.

Continuous Variable
A type of variable that can have an infinite number of values within a specified range; examples are height and weight.
Pearson correlation coefficient (r) used with this (-1 to 0 to +1; 0=no association).

Dose-Response Curve
The plot of a dose-response relationship, which is a type of correlative association between an exposure (e.g. dose of toxic chemical) and effect (e.g. a biological outcome).

Refers to the lowest dose at which a particular response occurs.

Multimodal Curve
A curve that has several peaks in the frequency of a condition.

The category in a frequency distribution that has the highest frequency of cases; there can be more than one mode in a frequency distribution.

Refers to the time period between initial exposure and a measurable response.

Epidemic Curve
A graphic plotting of the distribution of cases by time of onset. Related to point epidemics. It is a type of unimodal (having one mode) curve that aids in identifying the cause of a disease outbreak.

Any conjecture cast in a form that will allow it to be tested and refuted.

Method of Difference
Refers to a situation in which all of the factors in two or more domains are the same except for a single factor. The frequency of a disease that varies across the two settings is hypothesized to result from variation in a single causative factor.

Method of Concomitant Variation
Refers to a type of association in which the frequency of an outcome increases with the frequency of exposure to a factor.

Refers to the process of defining measurement procedures for the variables used in the study.

Multifactorial/Multiple Causality
Causal relationships that are involved with etiology of diseases involving more than one causal factor.

The process of passing from observations and axioms to generalizations. One of its goals is to draw conclusions about a parent population from sample-based data.

Confidence Interval Estimate
A range of values that with a certain degree of probability contain the population parameter.