How can you identify and correct logical fallacies in programs?

1 False Dichotomy

A false dichotomy is a logical fallacy that presents only two options or alternatives, when in reality there are more possibilities. For example, a program might assume that a user input is either valid or invalid, without considering other cases such as empty, incomplete, or ambiguous inputs. This can lead to errors or unexpected behavior when the program encounters inputs that do not fit the binary categories. To avoid false dichotomy, you should use more comprehensive and flexible conditions, validations, and error handling in your programs. For example, you can use elif or switch statements to handle multiple cases, or use try and except blocks to catch and handle exceptions.

3 Correlation and Causation

Correlation and causation is a logical fallacy that confuses the relationship between two variables or events, and assumes that one causes the other, when in fact they are only related or coincidental. For example, a program might use a statistical analysis to find a correlation between two variables, and then use that correlation to make predictions or recommendations, without considering other factors or variables that might influence the outcome. This can lead to inaccurate or misleading results, or overfitting or underfitting your data. To avoid correlation and causation fallacy, you should use more rigorous and robust methods, models, or algorithms to analyze your data and logic, and test your hypotheses and assumptions with experiments, simulations, or cross-validation.

4 Hasty Generalization

Hasty generalization is a logical fallacy that draws a conclusion based on insufficient or biased data or evidence. For example, a program might use a small or unrepresentative sample of data to make a generalization about a larger or more diverse population or group. This can lead to errors or biases in your programs, or missing or ignoring important information or variations in your data. To avoid hasty generalization, you should use more reliable and relevant data sources, methods, or techniques to collect, process, and analyze your data and logic, and use appropriate statistical measures, such as confidence intervals, margins of error, or significance tests, to evaluate your results and conclusions.

5 Slippery Slope

Slippery slope is a logical fallacy that assumes that a small or minor change or action will lead to a large or drastic consequence or outcome, without providing sufficient evidence or justification for the causal link. For example, a program might use a conditional statement to trigger a series of actions or events, without considering the probability, feasibility, or impact of each step or outcome. This can lead to unrealistic or exaggerated scenarios, or unintended or undesirable consequences in your programs. To avoid slippery slope, you should use more logical and realistic conditions, actions, and outcomes in your programs, and use safeguards, checks, or limits to prevent or mitigate potential risks or harms.

6 Appeal to Authority

Appeal to authority is a logical fallacy that relies on the opinion, reputation, or expertise of a person or a source, without verifying the validity, reliability, or relevance of their claims or arguments. For example, a program might use a library, a framework, or a tool, without checking its documentation, functionality, or compatibility, simply because it is popular, recommended, or endorsed by someone or something. This can lead to errors, vulnerabilities, or dependencies in your programs, or missing or overlooking better or more suitable options or solutions. To avoid appeal to authority, you should use more critical and independent thinking and evaluation in your programs, and use objective and empirical evidence, standards, or criteria to assess and compare your sources, tools, or options.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?