Fundamental Statistics for Life Scientists

These flashcards cover the key statistical concepts discussed in lectures on fundamental statistics for life scientists, focusing on types of variables, statistical measures, and methods of analysis.

Variables

Different types of characteristics that can be measured or categorized in research.

Mean

The average of a set of numbers, calculated by dividing the sum of values by the number of values.

Median

The middle value in a set of numbers when they are arranged in order.

Range

The difference between the highest and lowest values in a dataset.

Interquartile Range

The difference between the first quartile (25th percentile) and the third quartile (75th percentile) of a dataset.

Factor (qualitative)

A variable that reflects categories without a numerical value, e.g., sex or diet.

Covariate (quantitative)

A variable that can be measured numerically, such as rainfall or temperature.

Discrete Data

Data that can only take specific values and are counted, e.g., the number of students.

Continuous Data

Data that can take any value within a range, often measured, e.g., height or weight.

Correlation Analysis

A statistical method to determine whether a relationship exists between two variables.

General Linear Model (GLM)

A statistical framework used to describe the relationship between a response variable and one or more explanatory variables.

Response Variable

The dependent variable that researchers aim to understand or predict in an experiment.

Explanatory Variable

The independent variable used to explain variation in the response variable.

Null Hypothesis (Ho)

A statement that there is no effect or no difference, used as a default assumption in hypothesis testing.

Alternative Hypothesis (Ha)

A statement indicating the presence of an effect or difference, which researchers seek to support.

P Value

The probability of obtaining a result at least as extreme as the one observed if the null hypothesis is true.

Degrees of Freedom (df)

The number of independent values that can vary in an analysis without violating any constraints.

F-Ratio

The ratio of the mean sum of squares of an explanatory variable to the mean sum of squares of residuals in GLMs.

Standard Deviation

A measure of the amount of variation or dispersion of a set of values, indicating how spread out numbers are from the mean.

Variance

The average of the squared differences from the mean, providing a measure of how far each number in the set is from the mean.

Significance Level (\alpha)

The probability of rejecting the null hypothesis when it is true, commonly set at 0.05.

Type I Error

The error of rejecting a true null hypothesis (a 'false positive').

Type II Error

The error of failing to reject a false null hypothesis (a 'false negative').