Chapter 1 Statistical and Critical Thinking

Population

The complete collection of all measurements or data that are being considered.

Sample

A sub-collection of members selected from the population.

Context of the data

What do the data mean? What is the goal of the study?

Source of the data

Were the data from a source with a special interest?

Sampling Method

Were the data collected in a way that is unbiased?

Statistical significance

Achieved when a result is very unlikely to occur by chance.

Parameter

A numerical measurement describing some characteristic of a population.

Statistic

A numerical measurement describing some characteristic of a sample.

Quantitative data

Data consisting of numbers representing counts or measurements.

Discrete Data

Data where the number of possible values is either finite or countable.

Continuous Data

Data that has infinitely many possible values.

Qualitative Data

Data consisting of names or labels.

Nominal

Names, labels, or categories that cannot be arranged in any order.

Ordinal

Data that can be arranged in order but the differences are meaningless.

Interval

Data that can be arranged in order where the differences are meaningful.

Ratio

Interval level of measurement with 0 as a starting point.

Voluntary response survey

A self-selected sample that is not considered good.

Correlation and causation

Correlation does not imply causality.

Percentages

Can often be misleading, e.g., losing 300% of total weight.

Self-Interest Study

A study source with a special interest, e.g., a dental association checking if flossing is effective.

Observational Study

Observing and measuring specific characteristics without altering subjects.

Experiment

Applying treatment and observing its effects on subjects.

Simple Random Sample

A sample where every possible sample of the same size has the same chance of being chosen.

Random Sample

Members of the population are selected so each has an equal chance of selection.

Systematic Sampling

Select a starting point and then every kth element in the population.

Convenience Sampling

Using results that are easy to get, which is a bad practice.

Stratified Sampling

Subdivide the population into subgroups and draw a sample from each.

Cluster Sampling

Dividing the population into clusters and randomly selecting some clusters.

Confounding

Occurs when the experimenter cannot distinguish between effects of different factors.

Sampling Error

Difference between a sample result and the true population result due to chance sample fluctuations.

Non-Sampling Error

Errors from incorrectly collected, recorded, or analyzed data.