Find Something In Psychology That You Think Is

Find Something In Psychology That You Think Is

The assignment is this: find something in psychology that you think is problematic. This may be a theory or concept, a research finding, a methodology or practice, or any other issue relevant to the current state of affairs in psychology. Write a paper explaining this topic and what the problems are with it. Do not produce a simple opinion piece: Provide citations to back up your reasoning. Support your argument with evidence and clear reasoning.

You must use APA formatting rules as appropriate. Remember, the APA Publication Manual, 7th edition, is the only authoritative reference on APA style. There is no length requirement—write until you have completed making your point; page limits are arbitrary and meaningless. Tips: start with a statement in your opening paragraph that clearly describes the area of interest and the problem you have identified. Spend some time providing a descriptive background of the area of interest before detailing the problem and evidence.

In addition to supporting your criticisms, provide solutions, whether it be a different theory, or a revision to research methodology or statistical analysis, or proposals for new experiments to be done. Regarding references and APA formatting: write your paper as if you were going to submit it to a journal for peer-review and publication. Journals would require APA styled title pages, abstracts, page and paragraph formatting, citations and references. Be sure your choice of references would pass peer review. You should only be citing sources like the ones you see cited in other peer-reviewed articles, namely primary sources.

On Plagiarism: These papers will be submitted using the Turnitin tool built into webcourses. A similarity score will be calculated. See the Course Policy page for more information regarding course rules on what counts as plagiarism. Important: citing a source gives you license to describe the ideas and work performed by those authors. It does NOT give license to borrow or paraphrase their words.

Paper For Above instruction

Introduction

Psychology, as a scientific discipline, continuously evolves through the examination and refinement of theories, methodologies, and findings. Despite its progress, certain areas remain problematic due to methodological limitations, conceptual ambiguities, or replication issues. One such problematic area is the widespread reliance on p-values and null hypothesis significance testing (NHST) in psychological research. This reliance has led to various misunderstandings, overstated claims, and reproducibility crises that threaten the credibility of psychological science.

The Background and Importance of NHST in Psychology

Null hypothesis significance testing has been a dominant statistical method in psychology for decades. It involves testing whether observed data deviate significantly from what would be expected under a null hypothesis, typically indicating no effect or relationship (Wasserstein & Lazar, 2016). This approach allows researchers to make inferences about populations based on sample data, often using arbitrary significance thresholds such as p

The Problems with NHST and p-Values

One core problem with NHST is the overemphasis on the p-value as a definitive indicator of truth. A p-value indicates the probability of observing data as extreme as the sample, assuming the null hypothesis is true (Benjamin et al., 2018). However, it does not quantify the size or practical importance of an effect nor provide evidence for the alternative hypothesis. Consequently, researchers often misinterpret low p-values as confirming tangible effects, while high p-values are wrongly construed as evidence of no effect—a misconception known as the 'Null Hypothesis Significance Testing fallacy' (Amrhein et al., 2019).

Moreover, the arbitrary cutoff of p

Implications and Proposed Solutions

The problems associated with p-values and NHST necessitate reforms in statistical practice and research culture. One proposed solution is the adoption of estimation methods, such as confidence intervals (CIs), which provide a range of plausible values for effect sizes and are more informative than binary significance tests (Cumming, 2014). Emphasizing effect sizes and their confidence intervals can improve understanding of practical relevance.

Additionally, Bayesian statistical approaches offer an alternative framework that quantifies evidence for hypotheses and incorporates prior knowledge (Kruschke, 2015). This approach shifts focus from binary decisions to a continuum of evidence, reducing the likelihood of misinterpretation.

Furthermore, promoting open science practices—including data sharing, pre-registration, and replication studies—can mitigate publication bias and enhance reproducibility (Nosek et al., 2018). Journals should prioritize the publication of well-designed replication studies and null results, fostering a culture that values transparency over novelty.

Lastly, reforming statistical training in psychology is essential. Educators should emphasize the limitations of NHST, introduce alternative statistical methods, and foster critical interpretation skills among researchers (Diener & Diener, 2014). This cultural shift can help mitigate the problematic reliance on p-values and nurture more robust scientific practices.

Conclusion

The dependence on p-values and NHST in psychology is a significant and ongoing problem that hampers the replicability and credibility of research. The scientific community must embrace methodological reforms such as effect size estimation, Bayesian analysis, open science practices, and enhanced statistical education. By addressing these issues, psychology can improve its scientific rigor and continue advancing understanding of human behavior based on solid empirical foundations.

References

• Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567(7748), 305-307.
• Benjamin, D. J., Berger, J. O., Johannesson, M., et al. (2018). Redefining statistical significance. Nature Human Behaviour, 2(1), 6-10.
• Cohen, J. (1994). The earth is round (p
• Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7-29.
• Diener, E., & Diener, M. (2014). Goals for psychological science in the 21st century. Perspectives on Psychological Science, 9(2), 161-164.
• Ioannidis, J. P. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124.
• Kruschke, J. K. (2015). Bayesian data analysis. Cambridge University Press.
• Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Trends in Cognitive Sciences, 22(10), 889-900.
• Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716.
• Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129-133.