Final answer:
Errors such as splicing mutations in biology could lead to diseases like cancer, while misattribution and bias in social sciences distort data reliability. Correcting these issues involves rigorous validation and improvement of methodologies.
Step-by-step explanation:
Errors in experimental methods, as highlighted in the provided examples, can lead to severe consequences. In the context of biology, splicing errors due to mutations can have profound effects on human health, potentially leading to cancers and other diseases. A mutation that affects the splicing machinery or splicing sites can result in the improper removal or inclusion of exons and introns, leading to altered protein function or expression.
In a social or psychological study, inaccuracies like misattribution and bias can distort data. If Alejandra attributes a memory of watching a movie with Lucia when it was actually with someone else, this misattribution could affect the validity of any data or conclusions drawn from their experiences or testimonies. To correct this issue, the researcher should verify the source of information through cross-checking with other sources or by direct confirmation from the individuals involved.
In data collection for surveys, improvement is essential when errors are identified. Problems like biased survey questions, improper sampling methods, or lack of anonymity can affect the reliability and validity of the data. Conducting surveys with clear, neutral questions, random and representative sampling, and maintaining participant anonymity would improve the survey’s credibility.
It is crucial that researchers in any field acknowledge and correct these kinds of errors to maintain the integrity of their research and its findings.