¿Cómo se prueban y verifican los supuestos de análisis de datos? ------------------------?

Any good data analysis makes assumptions: - Do you expect conditions to improve? - Do you anticipate an uptick in customer behavior? - Is the data normally distributed? If you don't know your assumptions, people shouldn't trust your analysis.

I would consult with domain experts to validate the assumptions and interpretations based on the real-world context. Domain experts bring specialized knowledge and experience that can provide essential insights into the context and nuances of the data. Experts can help identify any biases in the data or the analysis approach that might skew results. They can confirm whether the assumptions made during the analysis are realistic and applicable to the specific domain. Their insights can lead to a more nuanced and thorough analysis, possibly suggesting additional variables or angles that had not been considered.

The first thing you need to understand the data based on the atributes then slowly you can build the hierarchy,on this we can develop our assumptions and correlate with each other

One thing I like to confirm about the data is what fields are end user responses, free-text, or filled based on other conditions. I'm highly suspect of free-text fields that could instead be given a defined data type or selection and would check those assumptions before assuming any larger trend about the data like distribution or regression.

Consulting with domain experts is a fantastic approach to validate assumptions and interpretations. Domain experts bring invaluable knowledge and experience that can provide crucial insights into the real-world context of the data. They can help identify any biases and ensure that the analysis approach aligns with the specific domain. Their expertise can lead to a more comprehensive and nuanced analysis, uncovering additional variables or perspectives that may have been overlooked. Collaborating with domain experts is a great way to enhance the credibility and depth of our analysis. 🧑🔬📊🌍

In my experience, this review process includes a detailed comparison of our assumptions against various facets of the data, such as its source, collection method, and any existing descriptions or research. It's essential to align our assumptions not just with the data but also with the broader context and objectives of our analysis. Whether it's addressing a specific research question, or solving a particular problem each assumption should directly contribute to these goals. A key aspect of this step is to critically evaluate whether our assumptions are in sync with the data we have. This involves questioning and challenging these assumptions to ensure they don't just fit our expectations but also accurately reflect the data's realities.

Have the mindset everything is an assumption. At time items we take for granted are actually assumptions and not necessarly tied to the data or knowns. It is important to note all the assumptions, and continuously review what is evidence driven vs what we assume is being driven by something. Noting assumptions can greatly assist when analyzing data and finding solutions. It helps identify common assumptions that may be leading to solutions being identified.

Reviewing assumptions is a critical step in the process of testing and verifying data analysis assumptions. It involves a thorough examination of the assumptions made at the outset of the analysis to ensure they align with the characteristics of the dataset and the goals of the study. This review should be an ongoing and iterative process throughout the analysis, allowing for adjustments and refinements as needed. Regularly revisiting assumptions helps in detecting any discrepancies or unexpected patterns that may challenge the validity of the analysis. By maintaining a vigilant stance on assumption review, analysts can enhance the robustness and reliability of their results, fostering a more accurate and trustworthy data analysis.

QQ plots are preferable technique in time series data distribution. Q-Q Plots are easy to visualise and interpret by non technical audience and senior management Most researchers use Q-Q plots to test the assumption of normality. In this method, observed value and expected value are plotted on a graph. If the plotted value vary more from a straight line, then the data is not normally distributed.

They are different tools and techniques can be used to test assumptions in data analysis. Descriptive statistics, such as the mean and standard deviation, can help to identify outliers or other unusual data points. Visualizations like histograms and scatterplots can be used to identify trends or relationships in the data. Hypothesis tests, such as t-tests and ANOVA, can be used to test whether an assumption is supported by the data. It is important to understand your data and choose the appropriate tool for testing.

Testing and verifying assumptions in data analysis is crucial for ensuring reliable outcomes. Initially, I inspect data distributions and employ descriptive statistics to get a sense of the data's behavior. For assumptions like normality, I use graphical methods like QQ plots alongside statistical tests like Shapiro-Wilk. To check homoscedasticity, I may use plots or tests like Levene's or Bartlett's. When examining relationships, scatter plots and correlation coefficients are handy. Furthermore, to validate predictive models, I split the data into training and testing sets to gauge the model's performance and ensure it generalizes well to unseen data.

Testing assumptions is a crucial phase in data analysis to ensure the reliability of results. This involves employing appropriate statistical tests or diagnostic procedures that specifically assess whether the assumptions made at the beginning of the analysis hold true for the given data. For instance, normality tests, residual analyses, or variance homogeneity tests can be conducted based on the nature of the assumptions. The results of these tests provide insights into the degree to which the data conforms to the assumed conditions.

The best approach to testing assumptions in data analysis involves a methodical process: Begin with descriptive statistics to understand data distributions. Use QQ plots and Shapiro-Wilk tests for assessing normality and apply Levene's or Bartlett's test for homoscedasticity. To analyze relationships, employ scatter plots and correlation coefficients. Crucially, split data into training and testing sets for predictive models, ensuring they generalize effectively to new data. This structured approach ensures that assumptions are rigorously tested and validated, leading to more reliable and robust analysis outcomes.

Verification of assumptions in data analysis involves a comprehensive assessment to confirm the validity and appropriateness of the underlying assumptions. This step includes validating assumptions through multiple approaches, such as visual inspections, statistical tests, or sensitivity analyses. By employing various verification techniques, analysts gain a nuanced understanding of how well the data aligns with the assumed conditions. If discrepancies are identified, further exploration and potential adjustments are made to enhance the robustness of the analysis.

Testing assumptions before employing a statistical method is essential because adjusting these assumptions is the primary purpose. If the assumptions for a particular model are not met, choosing to ignore them renders the results invalid. In such cases, it becomes imperative to adjust and opt for an alternative method with assumptions that align better with the data.

In data analysis, the flexibility to adjust assumptions is crucial when confronted with evidence suggesting deviations from the original expectations. If testing or verification reveals that certain assumptions are not fully met, analysts may need to consider adjustments or alternative approaches to better align with the characteristics of the data. This adaptability allows for a more realistic and accurate representation of the underlying relationships within the dataset. Adjusting assumptions based on empirical findings ensures that the analysis remains responsive to the intricacies of the data, contributing to a more robust and trustworthy analytical outcome.

Conveying statistical assumptions can be more challenging than sharing machine learning model results. Often, the audience may not be familiar with technical terms or may not have an interest in them. However, it's crucial not to overlook reporting these assumptions. My approach involves creating two reports: one with comprehensive details for reference, and another tailored for a non-technical audience, ensuring smooth comprehension for everyone involved.

It is important to familiar yourself with popular approaches to test common types of assumptions. For example, you may want to apply Runs test to test assumptions on the randomness of a data set. Another example is to consider apply intra-class correlation for testing assumptions on data independence in the first place.

In my daily work as a data analyst, testing assumptions is a crucial task that I can't overlook. Ignoring these assumptions would compromise the accuracy and validity of my results.

In data analysis, an assumption is a statement that is taken to be true, but which has not been proven. For example, you might assume that a certain dataset is representative of a larger population, or that a trend will continue into the future. These assumptions are necessary for the analysis to be meaningful, but they may not always be valid. It is important to test your assumptions and make sure they are valid, otherwise your analysis may be inaccurate or misleading.