EBIO1010- Statistics Exam 1

Average

• Referred to as the “mean”, an expectation for a randomly sampled individual

• Equal to the sum of each sampled individual from 1 to n individuals divided by the number of sampled individuals (n)

• Abstract

Standard deviation

• A measure of variation among individuals or things, the average distance of an individual from the mean

• The standard deviation is the square root of the variance

•  add up all the differences of individuals from the mean, divide by the number of individuals

Variance

the variance is equal to the sum of the squared differences between each sampled individual and the mean, for 1 to n individuals, divided by the number of sampled individuals (n) minus 1.

Effect

A measure of how something influences something else… how much (size / magnitude) and in what direction (positive or negative).

Uncertainty

An estimate of the degree to which we can know the true effect. A numeric value, or range of values, calculated from the data.

Causation

A statement explaining whether or not we can say one variable caused the effect observed in the other. (You will learn how to determine this later in the semester.)

Scope

Whether an inference is local and specific to a particular study or is more general and applies broadly across a variety of contexts (depends on the data)

The Argument Model

• A framework for making claims

• Necessary context for a reputable evidence-based claim (AKA the “pillars of inference”)

• Effect, Uncertainty, Causation, Scope

Estimate of the truth is what + what

Random Sampling error

• Chance variations that change unpredictably from one measurement to the next.

• Usually distributed evenly across mean

Systemic Sampling Error

• A consistent, predictable displacement of measurements away from the true value

• Usually distributed skewed across whole plot