To ensure accurate analysis after discovering fieldwork discrepancies, a rigorous review process is crucial. Begin by re-examining data collection methods and protocols, then verify data entry and transcription accuracy. Conduct thorough data cleaning and validation, checking for inconsistencies and outliers. Consult the fieldwork team for clarification on any issues. Implement quality control checks and audits, and utilize data visualization to detect anomalies. Document and justify all data corrections. By meticulously addressing discrepancies, you guarantee reliable analysis and informed decision-making, maintaining the integrity of your research.

When encountering discrepancies in fieldwork data, I prioritize cross-referencing with initial field notes and consult with team members who were involved in data collection to identify potential sources of error. In the office, I ensure accuracy by implementing a thorough data validation process, using software tools for consistency checks, and conducting multiple rounds of analysis. I also maintain clear documentation of any adjustments made, ensuring that decisions are transparent and traceable. By fostering open communication within the team and employing systematic verification methods, I ensure reliable and accurate analysis.

Discrepancies in data could be due to human or instrumental error,the best to take this in my opinion is to evaluate based on historic data and determine possible interpretations of the discrepancies.

Diante de discrepâncias nos dados de campo, nossa prioridade é a integridade da análise. Implementamos um processo rigoroso de verificação cruzada, combinando tecnologia avançada e expertise humana. Reunimos equipes multidisciplinares para revisar os dados, considerando variáveis contextuais do campo. Quando necessário, realizamos coletas adicionais ou validações in loco. Mantemos comunicação transparente com todas as partes envolvidas, transformando desafios em oportunidades de aprendizado e aprimoramento contínuo de nossos métodos. Assim, garantimos análises precisas e confiáveis, fundamentais para decisões assertivas e impacto positivo.

First you have to identify the source of the error. Reviewing field notes, chain of custody, laboratory data and QA/QC reports, equipment maintenance records, past reports and talking to field staff are all good resources. Then you can assess the impact on the overall data set. Could this error be repeated elsewhere? How does the error affect the accuracy of the data? Can it be corrected or accounted for? After you know how the dataset is impacted you can decide on the best resolution and determine if resampling/retesting needs to be done or, if the objectives of the project can be met despite the error. It is also helpful to review procedures and see if anything can be done to avoid similar errors in the future.

Al momento de realizar informes basados en trabajos de campo, es normal encontrar discrepancias o datos que no concuerdan. Sin embargo existen varias opciones para evitar que esto suceda o al menos disminuir al máximo las diferencias de datos entre sí o datos que no concuerdan con el balance general obtenido en terreno. Una de ellas puede ser la toma de notas precisa de cada observación relevante, así como registro fotográfico; y la más importante es realizar los informes lo más pronto posible, después de llegar de la actividad, con el fin de tener las experiencias frescas y no correr el riesgo de olvidar algún detalle importante. Puede sonar lógico, pero es muy importante!

I've seen alot of discrepancy in data in my field of work especially since I work with a multidisciplinary field of work and one thing I found helpful in dealing with these data is apply multisources approach where I look at literature review in past data to accurately decide why these discrepancies appeared and how to accurately get the right data to process it to a new information.

I would begin by thoroughly validating and cleaning the data, identifying outliers, inconsistencies, or missing values. Cross-referencing with other data sources, that potentially can help to confirm reliability. I also would consult with the field team to understand any collection issues, then decide whether to impute missing values or exclude unreliable data. Running sensitivity analyses would help assess the impact of discrepancies on results, and I would document all decisions and assumptions to maintain transparency and ensure a robust, defensible analysis.

First, it is necessary to know the type, size and importance of the discrepancies, then verify the sources, interview the field work team and verify these discrepancies and the mechanism followed during the field work. Then conduct a random check. To ensure the accuracy and uniformity of the data, the data is cleaned and revised to ensure that the data is consistent and harmonious. With the use of statistical techniques and conducting the analyses with the assistance of experts (if any). After ensuring that the discrepancies that were detected in the field work data are addressed. A clear SOPs are put in place as a precautionary measure with awareness to concern team. This is to avoid the reoccurrence of these discrepancies or similar ones.