{'fraction_non_alphanumeric': 0.03436426116838488, 'fraction_numerical': 0.0057273768613974796, 'mean_word_length': 4.56687898089172, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Simple Method for Removing Bias From a Popular Measure of Standardized Effect Size: Adjusted Partial Eta Squared

Accurate estimates of population effect size are critical to empirical science, for both reporting experimental results and conducting a priori power analyses. Unfortunately, the current most-popular measure of standardized effect size, partial eta squared ( η ^ p 2 ), is known to have positive bias. Two less-biased alternatives, partial epsilon squared ( ε ^ p 2 ) and partial omega squared ( ω ^ p 2 ), have both existed for decades, but neither is often employed. Given that researchers appear reluctant to abandon η ^ p 2 , this article provides a simple method for removing bias from this measure, to produce a value referred to as adjusted partial eta squared (adj η ^ p 2 ). Some of the many benefits of adopting this measure are briefly discussed.