{'fraction_non_alphanumeric': 0.04363636363636364, 'fraction_numerical': 0.01090909090909091, 'mean_word_length': 4.9, '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}}
Statistics in publishing: the (mis)use of the p-value (Part 2)

This is the second of a two-part article, in which we discuss problems associated with null hypothesis significance testing (NHST) and the use of the pvalue. In the first part, we described how NHST, and the p-value can be misused, the potential consequences and offered some practical solutions (Broekstra et al., 2022a). To illustrate this, we used a simulated dataset based on a randomized controlled trial (RCT) that investigated women with a distal radial fracture, randomized either to an intervention (castþ rehabilitation programme) or control (cast only) group (Broekstra et al., 2022b). In this second part, we will focus on some important conceptual problems of NHST and the p-value and propose an alternative solution to help address these problems.