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Scientific misconduct is the violation of the standard codes of scholarly conduct and ethical behavior in the publication of professional scientific research.A Lancet review on Handling of Scientific Misconduct in Scandinavian countries gave examples of policy definitions.
All loops must have fixed bounds. This prevents runaway code. Avoid heap memory allocation. Restrict functions to a single printed page. Use a minimum of two runtime assertions per function. Restrict the scope of data to the smallest possible. Check the return value of all non-void functions, or cast to void to indicate the return value is useless.
The European Code of Conduct for Research Integrity 2023 states, for example, the principles that, "Researchers, research institutions, and organisations ensure that access to data is as open as possible, as closed as necessary, and where appropriate in line with the FAIR Principles (Findable, Accessible, Interoperable and Reusable) for data ...
It is the violation of scientific integrity: violation of the scientific method and of research ethics in science, including in the design, conduct, and reporting of research. A Lancet review on Handling of Scientific Misconduct in Scandinavian countries provides the following sample definitions, [ 1 ] reproduced in The COPE report 1999: [ 2 ]
They can cause loss of information and resources that might result severely harmful for users, the general public, or employers. Therefore, software developers should minimize the risk of harming others due to coding errors, or security issues, by following standards to design and test systems (Code of Ethics and Professional Conduct). [3]
Medical ethics is an applied branch of ethics which analyzes the practice of clinical medicine and related scientific research. [20] Medical ethics is based on a set of values that professionals can refer to in the case of any confusion or conflict. These values include the respect for autonomy, non-maleficence, beneficence, and justice. [21]
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.
The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to variables as part of a larger expression. [106] In Python, == compares by value. Python's is operator may be used to compare object identities (comparison by reference), and comparisons may be chained—for example, a <= b <= c.