interpret_replication_results {mmibain}

Interpretation of Replication Study Results

Description

This function interprets the results of a replication study by applying Bayes factor (BF) and posterior model probabilities (PMPb) thresholds. It checks against predefined thresholds to determine if there is strong evidence for the hypotheses derived from the original study.

Usage

interpret_replication_results(
  replication_results,
  bf_threshold = 3,
  pmpb_threshold = 0.8
)

Arguments

replication_results

A list containing the results of a replication study, specifically a bain object with BF and PMPb values.

bf_threshold

The threshold for the Bayes factor (BF.c) above which the evidence is considered strong. Default is 3.

pmpb_threshold

The threshold for the posterior model probabilities (PMPb) above which the evidence is considered strong. Default is 0.80.

Details

The function first checks for the presence of a secondary hypothesis (H2) in the analysis results. If H2 is present, it will prioritize its interpretation; otherwise, it defaults to interpreting H1. Interpretation is based on whether the BF.c and PMPb values exceed their respective thresholds.

A 'win' result means there is strong evidence for the hypothesis, while a 'lose' indicates the evidence is inconclusive or not strong enough.

The function includes a disclaimer about the use of threshold values for hypothesis testing and recommends consulting the cited literature for a comprehensive understanding of Bayesian factors and informative hypothesis testing.

Value

A list with elements 'interpretation' providing the interpretative message, 'result' indicating a 'win' or 'lose' based on the interpretation, and 'disclaimer' providing a contextual disclaimer.

Note

The thresholds used in this function are for educational purposes within the context of a game and should not be taken as rigid rules for hypothesis testing in practice.

Examples

# Original study
os_deck <- deal_cards_to_rc_grid(n = 3)
original_study_data <- generate_study_data(os_deck, sample_size = 100)
original_study_results <- process_original_study(original_study_data)

# Replication study
rs_deck <- deal_cards_to_rc_grid(n = 3)
replication_data <- generate_study_data(rs_deck, sample_size = 100)
replication_results <- process_replication_study(replication_data,
                                                 original_study_results)

interpret_replication_results(replication_results)