Module 1
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Define “Sample”
Subset of individuals from a population of interest
Define “Estimation”
The ability to approximate an unknown quantity of a target population using sample data -All estimates have a sampling distribution.
Define “Parameter” Why is it subject to error?
Quantity describing a population from sample measurements/estimations. Subject to error due to usage of incomplete data (a sample)
Define” Random Sampling”
A sampling method that assures that the sample chosen from the population is chosen by giving everyone an equal and independent chance of being chosen. -Minimizes bias and allows for standard error calculations.
Define “Sampling error”, in terms of bias and independence. What is its relationship to precision?
A discrepancy that arises due to chance from sampling the population SE = 1/precision
Define “Convenience Sampling”, in terms of bias and independence.
A sampling method that chooses the sample group from individuals/groups that are easily available. -Introduces bias. -The sample being unbiased & independent is not guaranteed. -
Define “Volunteer Sampling”, in terms of bias and independence.
A sampling method that allows for the population of interest to give themselves up for sampling. -Introduces bias and can’t guarantee independence.
Why are larger samples better?
They are more precise and have lower sampling error
Define “Bias”
Discrepancy that arises due to the improper sampling of the population
What are the 2 major goals of sampling?
To reduce SE and bias & to allow for precision to be measured
Define “precision”
When the variables of the sampled population fall within the same range as one another (Clumped together).
Define “accuracy”
When the variables of the sampled population fall within/on the range of the true population (On the mark).
Define “Census”
The sampling of an entire population (rare)
Define “variables”
Characteristics that differ amongst individuals
Define “Categorical variables”
Qualitative measurement that can be sorted into groups
Define “Numerical variables”
Quantitative measurements. Two types: discrete (integers) and continuous (any real #).
Define “Nominal variable “
Categorical variables that have no inherent order (ex. colour of fur)
Define “Ordinal variable”
Categorical variable that has an order, despite no quantification (ex. small, medium, large).
Define “Interval variable”
A numerical variable that has an order on a numerical scale, with defined differences between points. No true 0. ex. year.
Define “Ratio Variable”
A numerical variable with defined ratios. True 0 (physically meaningful). ex. Mass.
Define “Observational study”
Nature assigns values, researches only observes activity and points to associations. No control of treatment assignment.
Define “Experimental study”
Researcher assigns treatment values randomly to individual units of study (reminder: a unit of study can be a group).
Define “ Explanatory variable”
Independent. Treatment being applied.
Define “Response variable”. How is it determined?
Dependent on the explanatory variable. Determined by examining associations between variables in test groups.