Question

label each transparent research practice with its alternative questionable research practice. harkingunderreporting null effectsp-hacking

Answer 1

- HARKing is paired with underreporting null effects.

- P-hacking is linked with underreporting null effects.

Step-by-step explanation:

HARKing (Hypothesizing After the Results are Known) is often associated with underreporting null effects in research practices. This occurs when researchers, after obtaining results, formulate or emphasize hypotheses post hoc to align with the observed outcomes, rather than presenting the original hypotheses and outcomes, leading to a potential distortion of the research process.

P-hacking, on the other hand, is commonly paired with underreporting null effects as well. P-hacking involves manipulating or selectively reporting statistical analyses to achieve a desired level of statistical significance. This questionable research practice can result in the underreporting of null effects or the inflation of statistical significance, leading to biased and misleading conclusions.

The impact of HARKing and P-hacking on the integrity of research and ways to promote transparent and ethical research practices in various fields. Understanding alternative practices helps maintain the credibility and reliability of research findings.

Answer 2

HARKing, underreporting null effects, and p-hacking are all questionable research practices that can impact the reliability of data.

Step-by-step explanation:

HARKing (Hypothesizing After the Results are Known) is a questionable research practice where researchers develop a hypothesis after analyzing the data. This can impact the reliability of the resulting data because it introduces bias and increases the likelihood of finding false positive correlations.

Underreporting null effects is another questionable research practice where researchers fail to report or publish results that show no significant effect. This can lead to publication bias and skew the overall scientific literature towards positive findings.

P-hacking (data dredging or cherry-picking) is a practice where researchers manipulate the data or analytical procedures until a statistically significant result is obtained. This can impact the reliability of the resulting data by inflating the chances of Type I errors and false positive findings.