Collecting Data

It limits inferential capability to confirm causality despite establishing temporal precedence and controlling for confounding variables

It enhances causative determinations by exclusively attributing variations to the treatment effect and inherent randomness in the process

It precludes the necessity of considering external causal factors given the sufficiency of internal validity provided by random assignment alone

It ensures the complete removal of subjectivity in decision-making, leading to pristine, objectified conclusions regarding cause-effect relationships

To estimate the range within which a population parameter lies based on sample data.

To calculate the exact value of a population parameter from sample data.

To prove that one categorical variable has an effect on another.

To determine if there is a statistically significant difference between two sample means.

The total variance for all samples combined must equal twice that of each separate sample's variance.

Heterogeneity of variances, meaning both populations have different but known variances.

Variance equality within samples rather than across populations being compared.

Homogeneity of variances, meaning both populations have equal variances.

Larger p-value inaccuracies are detected as larger deviations from the normal distribution occur in insignificant ways.

P-value discrepancies are irrelevant as long as non-normal distributions sampled resemble a bell-shaped curve closely enough to pass muster.

Smaller p-value inaccuracies are detected as smaller deviations from the normal distribution occur in significant ways.

No discernible pattern emerges in terms of accuracy, as measured through the prism of deviation from the norms set forth at the beginning phase of experimental manipulation.

Random assignment to treatment groups

Matching participants on specific characteristics

Conducting the experiment in a controlled environment

Using a large sample size for the experiment

Systematic sampling

Multistage sampling

Cluster sampling

Stratified random sampling

Usage of sophisticated statistical jargon that may not easily be interpreted by general audiences

A statement about the practical significance of the results in relation to real-world applications

Implementation of the standard deviation and estimates without explaining their relevance

A comment on the p-value being larger or smaller than the significance level without contextualization

Smaller random samples

Convenience samples

Voluntary response samples

Larger random samples

Measurement error

Type II error

Type I error

Sampling error

The variability within blocks should be equal to the variability between treatments.

The variability within blocks should be less than the variability between treatments.

The variability within and between treatments should not be compared in a randomized block design.

The variability within blocks should be greater than the variability between treatments.