Você está enfrentando resultados conflitantes de análise de dados. Como você navega pelas interpretações?

When facing conflicting data analysis results, investigate the root causes of discrepancies. Different stakeholders have varying priorities: Sales focus on sales volume and revenue, while Finance looks at profitability. Conflicts can often arise from different data interpretations, such as aggregating data by sales date versus ship date. Address assumptions and open a dialogue to uncover these differences. Aim to reconcile analyses by creating a unified view. Remember, the goal is to provide a comprehensive understanding of the data landscape. By fostering collaboration and transparency, you can turn conflicts into opportunities for deeper insights and informed decision-making.

O primeiro passo é verificar os dados e a limpeza realizada no mesmo, deve se certificar que não existem "outliers", aqueles dados que se desviam exorbitante ou até mesmo não são significativo, por exemplo meus dados são para previsão de venda de um imóvel em determinada região, mas não foi feita limpeza corretamente dos dados e ao invés de ter somente o valor de venda dos imóveis tem também o valor do aluguel, neste caso o valor do aluguel é um "outlier".

É fundamental conhecer o banco de dados de onde estão sendo extraídas as informações, pois podem existir colunas com informações semelhantes que, se usadas erroneamente, certamente resultarão em conclusões equivocadas.

I support the idea or comparing methods. By doing this one knows the strengths and weaknesses of different methods and there one is able to choose the best method

It is always important to have a new perspective. In an editorial, we call it "Writer's blindspot" -- similarly discussing the conflicting results, reviewing methodologies, and validating assumptions to identify potential sources of discrepancies. Bringing in fresh perspectives can help uncover overlooked factors or errors, leading to a more accurate and comprehensive understanding. Collaborating with experts ensures a thorough examination of the data, ultimately guiding to a well-supported conclusion.

Nós devemos ter ciência, que por mais que tenhamos conhecimentos no tratamento dos dados, o especialista da área é que tem o domínio e pode informar a importância daquele dado ou o descarte do mesmo.

If you get an analysis where there seems to be something off, the first thing you should do is go back to basics. Start by understanding the problem at hand (what are we trying to study?) and the methods of proposed measurement (how did we go about measuring our phenomena of interest?). Once we have that, review the process of analysis, check assumptions for the tests involved and try to propose other methods of analysis. If we follow these simple steps, the flaw in our reasoning will usually show up. It is necessary to steer our actions away from falling into confirmation bias pitfalls. It helps (a lot) to consult with a colleague. Fresh eyes on the problem usually yield great results.