What are the most common statistical model building mistakes?

Building a statistical model without exploring and understanding your data is akin to attempting surgery without understanding the patient's anatomy. It's a recipe for disaster. Data exploration acts as a flashlight, illuminating critical patterns, outliers, missing values, and correlations that can profoundly impact your model's success. Ignoring these hidden gems can lead to skewed results, inaccurate conclusions, and a virtually useless model. Furthermore, it can lead to disastrous misalignment between business requirements and model output, wasting resources and missing opportunities.

To my experience, one of the most commong mistakes is Ignoring Assumptions. Many statistical models have underlying assumptions that must be met for the model to be valid. Ignoring these assumptions (e.g., normality of residuals in linear regression) can lead to inaccurate conclusions.

Selection Bias is overlooking biased data collection to skew model representation. Multicollinearity is ignoring high predictor correlations to impact model stability. Outliers and Anomalies are unaddressed anomalies to affect model performance. Var Selection is to include irrelevant variables to add noise to the model. Lack of Cross-Validation is neglecting validation techniques for overfitting. Assumption Validation is ignoring model assumption checks to impact reliability. Non-Standardized Variables are unstandardized variables to hinder model convergence. Ignoring Interactions is overlooking predictor interactions yields incomplete models. Model Transparency is failure to clarify model rationale and limitations to hamper trust.

Common statistical model building mistakes include overfitting, where models are too complex and fit noise instead of signal, leading to poor generalization on new data. Underfitting, where models are too simple to capture underlying patterns, is also prevalent. Ignoring data assumptions like normality, independence, or homoscedasticity can invalidate model results. Neglecting to properly handle missing or outlier data often skews outcomes. Relying solely on p-values without considering effect size or practical significance can be misleading. Not validating models with out-of-sample data or cross-validation can result in overestimating model accuracy.

Representation bias will dismantle your model before it is built. If you do not have a representative sample, consider techniques to train with out access to possible data configurations.

Overfitting is a common data science pitfall, where a model "overlearns" from training data, even capturing noise and anomalies. This results in poor performance when new data is introduced. It usually stems from an overly complex model with many features. Techniques like cross-validation and regularization can help prevent overfitting. Beyond striving for accuracy in training data, the goal is a robust model that can reliably handle new, unseen data.

Underfitting is the converse of overfitting, implying that the model is overly simplistic or too all-encompassing for the data. This can lead to poor accuracy, high bias, and low significance. Increasing model complexity, using more features, variables or parameters, and evaluating the model on various metrics can counter underfitting. The objective is to create a model that, while intricate enough to capture pertinent patterns in the data, still maintains robustness and reliability.

Dois pressupostos muito negligenciados são: Normalidade dos Resíduos: Muitos modelos estatísticos, como regressão linear, assumem que os resíduos (diferenças entre os valores observados e os previstos) são normalmente distribuídos. A violação desse pressuposto pode afetar a precisão dos intervalos de confiança e dos testes de hipóteses. Homocedasticidade (Homogeneidade das Variâncias): A variabilidade dos resíduos deve ser constante em todos os níveis da variável independente. Se a variabilidade dos resíduos não for constante, os resultados do modelo podem ser enviesados.

Violating assumptions in statistical models, such as normality or homogeneity, can skew results and conclusions. For example, making predictions using a regression model that doesn't meet its required conditions can lead to unreliable results and overconfident predictions. As data scientists, it's crucial to understand and test these assumptions through residual analysis, data plotting, and statistical tests. Ensuring alignment between our data and model's assumptions is key to achieving an accurate representation of reality.

Assuming the overall data and information is dependent or independent applying may vary especially the process immediately So assumption of database will conclude in some day not earlier

Understanding practical model implementation requirements such as containerisation and DevSecOps is essential to deploy a model. While less discussed, a deep foundational understanding is required to ensure that statistical assumptions underpinning models are adhered to. Without this, a range of model output errors, such as biased estimates, may be realised, which can impact outcome quality and statistical power. While in some circumstances, these impacts may be negligible, often, the interpretation of results is fundamentally changed.

In my experience the final report/presentation's deliverance is one of if not the most critical point that can make or break the whole process as a success or a failure. Given that most of the time models we build are presented to the people who have little to no idea how they are done, simplicity is the key. Clearly conveying the "why" of the steps of the model building, explaining validation and verification are essential. It should be noted communication is needed in every step not just at the level of presentation of finished work. As most experts know and say loading your communication medium with long sentences and paragraphs instead of visuals is an easy mistake to be avoided.

Consider using meetings, written documents, technical documentation, and 'cheat sheets' to indicate the value of what you have worked on!

Communication of various fields and extraction of background is very important the audience will also expect the maximum value of data and information So communication of data can implement to near the may cause to accumulate and accuracy

Focus on understanding the problem by creating a functional block diagram and consulting at least 3 persons to get their perspective. Next try to invent a new or modified algorithm based on standardized models.

These days, it also necessary to review the model for bias, the transparency and explainability of the model and how other aspects of AI safety can be incorporated into the model. The social license of the model is an important factor to consider. Building a model which is audited properly for AI safety also helps to prevent exposure to legal liability at a later stage. A good example is that large language models now need to have their content filters reviewed to prevent the outputs from delivering hateful, prejudicial or illegal content.

Great minds and contributors already did justice to the subject matter. However, when dealing with uncertainties that may arise from statistical models, in a bid to incorporate probabilistic model, wrong use of distributions and functions can mar the whole model process. The mistake of randomization is another topic extension in this subject matter