Specious reasoning is misleading since it seems correct at first, but fails to standup to careful analysis. Thoughts grounded on non-authoritative judgments and random guessing are often considered specious. Inferences that were developed speciously flop when carefully scrutinized.
Assume, for example, you are Portuguese Translation Manager at a global manufacturing company. The president of your company has directed you to evaluate the reliability of pre-employment evaluation testing of workers in Portugal and Brazil as a measure of intellect and as an indicator of future employee success. Going over the evidence that you gathered, you discover a strong positive correlation between below average pre-employment evaluation scores and low achievers. After doing so, you substantiate your findings by evaluating and analyzing a solid cross section of trustworthy information sources. Once you substantiate your findings, you determine that you are prepared to conclude that pre-employment evaluation tests are reliable measures of intelligence and predict future employee performance. Unfortunately, your analysis would be misleading unless you could show that:
- Only candidates for jobs having a high potential for promotion were given pre-employment tests
- Candidates in Portugal and Brazil had later been exposed to the exact same training curriculum at a similar pace.
It’s important for translation services workers to understand that even hard facts are sometimes used to support faulty reasoning. The data that is gathered must be interpreted correctly, objectively, and within a context that accounts for unforeseen variables.