Collecting Data

Treatment imposition

Manipulation of explanatory variables

Random assignment

Use of a control group

Measuring plant growth only at the end of the growing season.

Ensuring that both fertilizers are applied under similar environmental conditions.

Observing only a specific species of plants for uniformity reasons.

Applying different amounts of each fertilizer based on plant type.

Welch's t-test

One-sample t-test

Chi-squared test for independence

Paired samples t-test

Using only volunteer students as subjects.

Pairing subjects based on their GPAs.

Random assignment of subjects to treatments.

Assigning treatments based on class schedules.

No treatments, only control groups

Both treatments in a random order

Only one treatment randomly assigned

Only one treatment, predetermined by the researcher

Interquartile range

Mean

Standard deviation

Range

They are the variables that are intentionally changed.

They are the set of individuals who are used as a baseline comparison.

They are the outcomes that are measured by the researcher.

They influence the response variable but are not manipulated or controlled by the researcher.

Using matched pairs where each subject serves as their own control.

Conducting a completely randomized design without any form of blocking or pairing.

Assigning all subjects randomly without considering their characteristics.

Blocking based on similar characteristics before randomly assigning treatments.

Variance

Z-score

Median

Mean

The main purpose of blocking is to establish causal relationships between multiple dependent variables simultaneously.

Mainly, blocks serve to simplify analysis, allowing for one-way ANOVA instead of complex multivariate techniques.

Blocks are used to create groups of similar individuals which control for outside factors.

Blocking is primarily useful for eliminating natural factors such as weather or time of day.