Determining if the frequency distribution of a given data set follows a normal distribution or not is among the first steps of data analysis. Visual examination of the data, commonly by Q-Q plot, although is acceptable by many scientists, is considered subjective and not acceptable by other researchers. One-sample Kolmogorov-Smirnov test with Lilliefors correction (for a sample size ≥ 50) and Shapiro-Wilk test (for a sample size < 50) are common statistical tests for checking the normality of a data set quantitatively. As parametric tests, which assume that the data distribution is normal (Gaussian, bell-shaped), are more robust compared to their non-parametric counterparts, we commonly use transformations (e.g., log-transformation, Box-Cox transformation, etc.) to make the frequency distribution of non-normally distributed data close to a normal distribution. Herein, I wish to reflect on presenting how to practically work with these statistical methods through examining of real data sets.

Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment.This will result in a more accurate (less biased) estimate of the treatment effect.

Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment.This will result in a more accurate (less biased) estimate of the treatment effect.

Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment.This will result in a more accurate (less biased) estimate of the treatment effect.

Making correct decisions is of paramount importance in clinical medicine and health-related disciplines. Randomized clinical trials are considered the gold-standard type of study for the assessment of the efficacy of a treatment. However, conducting a randomized clinical trial is not always possible; observational studies should be conducted, instead. For lack of randomization in observational studies, there may be a priori differences in the distributions of certain variables (e.g., age, race, and quality of health care services) between the study groups that may result in a biased estimate of the outcome of interest. Risk adjustment methods are used to account for these a priori differences and find an unbiased measure of the treatment effect. The method involves several steps including defining the outcome of interest and identifying its potential outcome predictors. Then, we need to operationalize the selected risk factors and construct a statistical model or other methods of adjustment.This will result in a more accurate (less biased) estimate of the treatment effect.

Plagiarism is among commonly identified scientific misconducts in submitted manuscripts.Some journals routinely check the level of text similarity in the submitted manuscripts at the time of submission and reject the submission on the fly if the text similarity score exceeds a set cut-off value (e.g., 20%). Herein, I present a manuscript with 32% text similarity, yet without any instances of text plagiarism. This underlines the fact that text similarity is not necessarily tantamount to text plagiarism. Every instance of text similarity should be examined with scrutiny by a trained person in the editorial office. A high text similarity score does not always imply plagiarism; a low score, on the other hand, does not guarantee absence of plagiarism. There is no cut-off for text similarity to imply text plagiarism.

Plagiarism is among the prevalent misconducts reported in scientific writing and common causes of article retraction in scholarly journals. Plagiarism of idea is not acceptable by any means. However, plagiarism of text is a matter of debate from culture to culture. Herein, I wish to reflect on a bird’s eye view of plagiarism, particularly plagiarism of text, in scientific writing. Text similarity score as a signal of text plagiarism is not an appropriate index and an expert should examine the similarity with enough scrutiny. Text recycling in certain instances might be acceptable in scientific writing provided that the authors could correctly construe the text piece they borrowed. With introduction of artificial intelligence-based units, which help authors to write their manuscripts, the incidence of text plagiarism might increase.However, after a while, when a universal artificial unit takes over, no one will need to worry about text plagiarism as the incentive to commit plagiarism will be abolished, I believe.

Background: With emergence of chatbots to help authors with scientific writings, editors should have tools to identify artificial intelligence-generated texts. GPTZero is among the first websites that has sought media attention claiming to differentiate machine-generated from human-written texts. Methods: Using 20 text pieces generated by ChatGPT in response to arbitrary questions on various topics in medicine and 30 pieces chosen from previously published medical articles, the performance of GPTZero was assessed. Results: GPTZero had a sensitivity of 0.65 (95% confidence interval, 0.41–0.85); specificity, 0.90 (0.73–0.98); accuracy, 0.80 (0.66–0.90); and positive and negative likelihood ratios, 6.5 (2.1–19.9) and 0.4 (0.2–0.7), respectively. Conclusion GPTZero has a low false-positive (classifying a human-written text as machinegenerated) and a high false-negative rate (classifying a machine-generated text as human-written).