Here's how you can harmonize intuition and data-driven decisions in Statistics.

It's very important to leverage insights from data to validate intuition or assumptions. For example if you believe that August has been always your best sales month and you expect the same today, it's important to validate this belief with current data to check trend, otherwise it could indicate a different trajectory. Expert judgement would be vital in fields where insights might not seem obvious from the perspective of data alone.

Leverage intuition to identify promising research angles or variables, but validate these insights through data triangulation. For example, if intuition suggests a potential link between patient lifestyle and treatment efficacy, use various data sources—such as clinical records, patient surveys, and external studies—to confirm this relationship. Additionally, conduct exploratory data analysis to uncover underlying patterns that support or challenge your initial instincts. This process not only reinforces your hypothesis but also broadens your understanding by integrating multiple perspectives and data sources.

Trusting your intuition is great when you gained a lot of experience in any field, not just in statistics. If you feel you have enough experience in statistics then trust your gut and make decisions based on intuition but if you feel you haven't got enough experience...then it's best to make data driven decisions.

Quando você entende do negócio já consegue com seu instinto pegar atalhos que ir?o ajudar na tomada de decis?o, porém qualquer estudo precisa ser baseado em dados que ir?o provar ou n?o a sua tese.

In healthcare, a hospital might hypothesize that high readmission rates are due to patient non-compliance. However, by embracing data, they analyze patient demographics, discharge processes, and follow-up care. The data reveals that inadequate post-discharge support is the key factor, not non-compliance. Armed with this evidence, the hospital implements targeted interventions and successfully reduces readmission rates. This example shows how relying on data can refine hypotheses, challenge assumptions, and drive more effective decision-making.

O ponta pé inicial de qualquer análise sempre ser?o os dados, por isso entender suas estruturas, comportamentos e variáveis, com certeza ir?o contribuir para seus estudos. Lembre se que com esse conhecimento você poderá inferir qual melhor software para análise, quanto tempo em média pode levar seu estudo e custo que será gerado.

In healthcare, a doctor might intuitively believe that a new medication reduces recovery time for patients with a specific condition. This hypothesis is based on years of experience and observation. To test it, the doctor collects data from patients using the medication and compares it to those who aren’t. The data reveals that while recovery times are reduced, it’s actually due to a combination of the medication and improved follow-up care. This finding prompts the doctor to adjust their hypothesis and focus on a more comprehensive treatment plan. By balancing intuition with data, the doctor refines their approach, leading to better patient outcomes.

Na maioria das vezes que come?o um estudo, já crio alguns caminhos que entendo que possam ser seguidos, mas somente com o aprofundamento nas análises é que confirmo se estava certo ou n?o. No melhor cenário, que é quando minha percep??o está alinhada com os dados eu simplesmente sigo em frente ou termino a análise, dependendo de onde estou. Porém quando os dados mostram outro caminho, preciso dar uns passos para trás e recome?ar.

In healthcare, a seasoned physician might believe that a particular symptom always indicates a specific disease, a conviction shaped by years of experience. This is an example of confirmation bias, where only supporting evidence is noticed. To challenge this, the physician gathers comprehensive patient data and conducts rigorous statistical analysis. The results reveal that the symptom is linked to multiple conditions, not just the one initially assumed. This prompts a reassessment of diagnostic criteria, leading to more accurate diagnoses and treatments. By confronting biases with data, healthcare providers can make more objective decisions, ultimately improving patient care.

Ter um viés, até pela experiência já praticada pode vir a contribuir para o estudo, mas esse viés precisa ser comprovado com os dados e o analista ter a capacidade de reconhecer que seu viés estava equivocado quando os números mostrarem outro caminho.

In healthcare, a research team hypothesizes that a new protocol will reduce infection rates in a hospital ward. After implementing the protocol, initial data shows only a modest decrease. Rather than abandoning the approach, the team reviews the data, refines their hypothesis, and makes adjustments to the protocol. They conduct further testing, collect more data, and notice a significant reduction in infections over time. This iterative process not only improves the protocol but also sharpens the team’s intuition about what works. By continuously learning from both data and experience, they enhance their decision-making, leading to better patient outcomes and more effective healthcare strategies.

In healthcare, a research team discovers that a new treatment significantly reduces recovery time for patients. To gain support for this finding, they must communicate their results effectively. The team creates a presentation with clear charts showing the treatment’s impact and uses simple language to explain the statistical significance. They also share stories of patients who have benefited, blending data with real-world impact. This approach helps both technical and non-technical audiences grasp the importance of the findings, fostering trust in the decision-making process and highlighting the power of combining intuition with data-driven analysis.

Uma boa comunica??o será fundamental para você passar a mensagem sobre seus feitos e descobertas, lembrando que uma mensagem bem clara ajuda a diminuir a quantidade de dúvidas e também pode ajudar com que outras pessoas com experiências parecidas possam a vir contribuir no seu material.