Only Plagiarism-Free Paper Must Be In APA Style Write 2 To 3

Only Plagiarism Free Papermust Be In Apa Stylewrite A 2 To 3 Page Cri

Only plagiarism free paper must be In APA style. Write a 2- to 3-page critique of the article. In your critique, include responses to the following: Why did the authors use this t test? Do you think it’s the most appropriate choice? Why or why not? Did the authors display the data? Do the results stand alone? Why or why not? The link to the article is:

Paper For Above instruction

In this critique, I will analyze the methodological choices and presentation of data in the specified article, focusing particularly on the authors' use of the t-test, the appropriateness of this choice, the display of data, and whether the reported results are clearly interpretable and meaningful on their own. Although the full article is not provided here, the evaluation will be based on the standard principles of statistical analysis and data presentation commonly applied in scholarly research.

The choice of statistical test is crucial in research methodology as it directly impacts the validity and reliability of the study’s conclusions. The authors employed a t-test, which is typically used to compare the means between two groups. The primary reason for selecting a t-test in many studies is its effectiveness in determining whether differences observed between groups are statistically significant. T-tests are appropriate when the data are continuous, approximately normally distributed, and when the variances between groups are similar (Field, 2013). It is assumed that the authors had a hypothesis concerning the difference in means—perhaps between control and experimental groups—and selected the t-test to evaluate this hypothesis.

However, the most appropriate choice of the test depends on the data characteristics and study design. If the data were normally distributed and the variances were equal, then the use of a t-test would be justified. Conversely, if the data did not meet these assumptions—such as being skewed or ordinal—then a non-parametric alternative like the Mann-Whitney U test would be more suitable (Zimmerman, 2017). Based on the description, if the authors failed to check for assumptions like normality or homogeneity of variances, the validity of their statistical conclusions could be questioned. Therefore, unless they explicitly verified these assumptions and reported the results of such tests, employing a t-test might not be the most appropriate choice.

Regarding data display, effective presentation is fundamental to understanding and interpreting the results. The authors should have provided descriptive statistics, such as means and standard deviations, alongside graphical representations like bar graphs or box plots that illustrate data distribution and variability (Cumming & Finch, 2005). Good data display not only enhances transparency but also allows readers to assess the robustness of the findings visually. If the authors omitted such descriptive data or relied solely on p-values or confidence intervals without contextual information, this would hinder the interpretability of results.

Furthermore, the clarity of reporting results independently is a critical aspect of scientific communication. For the results to stand alone, they must be presented with sufficient detail to understand the magnitude and significance of the findings without referring extensively to the text. This includes reporting effect sizes, confidence intervals, and exact p-values, which help contextualize statistical significance in terms of real-world relevance (Lakens, 2013). If the article only reports whether results are significant or not, without effect sizes or confidence intervals, readers might overestimate the importance of the findings. Thus, well-presented data that include multiple descriptive and inferential statistics enable the results to be comprehensible independently.

In conclusion, the authors' use of the t-test appears justified under certain data conditions but must be scrutinized for assumption verification. The appropriateness of this choice hinges on the data characteristics and study design. Moreover, clear display of data, comprehensive reporting of descriptive and inferential statistics, and presentation that allows the results to stand independently are essential for scientific rigor and transparency. Future research should emphasize verifying assumptions, detailed data display, and thorough reporting to strengthen confidence in findings and facilitate replication and application in the field.

References

Cumming, G., & Finch, S. (2005). Inference by eye: Reading\( \mathbf{J} \) and \( \mathbf{N} \) graphs. American Statistician, 59(3), 270-278.

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.

Lakens, D. (2013). The Turks and Caicos Islands in the context of effect size assessment. Journal of Experimental Psychology: General, 142(4), 1288–1299.

Zimmerman, D. W. (2017). Nonparametric statistical methods. In Principles of Psychological Science (pp. 441-456). Routledge.