Abstract
We present the assumptions and philosophy underlying artificial psychology (AP) and motivate the need for a set of models that can incorporate information from complex mental systems using ideas such as fuzziness of a system and unsupervised algorithms that use artificial intelligence. We discuss the need for a multiplicity of modeling approaches to help us to understand the world. We mention issues involving hypothesis testing; in particular, we introduce and define the p-value and highlight its shortcomings, including p-hacking, where data is manipulated so that it yields statistically significant results with an associated bias toward publishing studies that have p-values below a certain threshold. We mention widespread misunderstandings in the interpretation of the p-value and associated dangers, such as giving the impression that the world is “black and white,” and motivate the need for complementary more nuanced approaches to testing statistical hypotheses that can overcome these deficiencies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chang, M., Balser, J., Roach, J., & Bliss, R. (2019). Innovative strategies, statistical solutions and simulations for modern clinical trials. CRC Press/Taylor & Francis Group.
Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 25, 7–29.
Farahani, H., Azadfallah, P., Chesli, R. R., Pourmohamadreza-Tajrishi, M., Esrafilian, F., Lavasani, F. F., & Chiniforoushan, F. (2021a). Methodology of inquiring “therapy failure” in psychotherapy research: Practical guide for clinical practitioners and researchers. Psychotherapy, 7, 01.
Farahani, H., Azadfallah, P., Watson, P., & Blagojević, M. (2021b). Bayesian hypothesis testing in linear models: A case study predicting mental health. https://doi.org/10.13140/RG.2.2.32071.37283
Farahani, H., Nápoles, G., & Azadfallah, P. (2021c). Fuzzy cognitive maps for impact assessment in psychological research: Case study of psychological well-being. In 3th international conference on modern approach in humanities and social sciences (ICMHS).
Francis, G. (2014). The frequency of excess success for articles in psychological science. Psychonomic Bulletin & Review, 21(5), 1180–1187.
Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., & Kruger, L. (1990). The empire of chance: How probability changed science and everyday life (Vol. 12). Cambridge University Press.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31, 337–350.
Harrison, A. J., McErlain-Naylor, S. A., Bradshaw, E. J., Dai, B., Nunome, H., Hughes, G. T. G., Kong, P. W., Benedicte Vanwanseele, J., VilasBoas, P., & Fong, D. T. P. (2020). Recommendations for statistical analysis involving null hypothesis significance testing. Sports Biomechanics, 19(5), 561–568. https://doi.org/10.1080/14763141.2020.1782555
Huette, S., Winter, B., Matlock, T., & Spivey, M. (2012). Processing motion implied in language: Eye-movement differences during aspect comprehension. Cognitive Processing, 13(1), 193–197.
Indrayan, A. (2019). The conundrum of P-values: Statistical significance is unavoidable but need medical significance too. Journal of Biostatistics and Epidemiology, 5(4), 259–267.
Lyu, Z., Peng, K., & Hu, C.-P. (2018). P-value, confidence intervals, and statistical inference: A new dataset of misinterpretation. Frontiers in Psychology, 9, 868.
Lyu, X.-K., Xu, Y., Zhao, X.-F., Zuo, X.-N., & Hu, C.-P. (2020). Beyond psychology: Prevalence of p value and confidence interval misinterpretation across different fields. Journal of Pacific Rim Psychology, 14. https://doi.org/10.1017/prp.2019.28
Maxwell, J. A. (2004). Causal explanation, qualitative research, and scientific inquiry in education. Educational Researcher, 33(2), 3–11.
Morey, R. D., Hoekstra, R., Rouder, J. N., & Wagenmakers, E.-J. (2016). Continued misinterpretation of confidence intervals: Response to Miller and Ulrich. Psychonomic Bulletin & Review, 23, 131–140.
Schmidt, F. L., & Oh, I. S. (2016). The crisis of confidence in research findings in psychology: Is lack of replication the real problem? Or is it something else? Archives of Scientific Psychology, 4(1), 32.
Ziliak, S., & McCloskey, D. N. (2008). The cult of statistical significance: How the standard error costs us jobs, justice, and lives. University of Michigan Press.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Farahani, H., Blagojević, M., Azadfallah, P., Watson, P., Esrafilian, F., Saljoughi, S. (2023). In Search of a Method. In: An Introduction to Artificial Psychology. Springer, Cham. https://doi.org/10.1007/978-3-031-31172-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-31172-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31171-0
Online ISBN: 978-3-031-31172-7
eBook Packages: Behavioral Science and PsychologyBehavioral Science and Psychology (R0)