What are the key differences between statistical analysis and machine learning?

Statistical analysis and machine learning differ in objectives, approach, and application. Statistical analysis aims to understand data relationships and test hypotheses using structured datasets and rigorous mathematical frameworks. It emphasizes interpretability with simpler models like linear regression. In contrast, machine learning focuses on building algorithms that learn from data to make predictions without explicit programming. It handles various data types, including unstructured data, and prioritizes predictive accuracy over interpretability, employing techniques like deep neural networks. While statistical analysis is prevalent in fields like economics and psychology,

I perceive machine learning as a small part of the vast field of statistics. When statistical models are implemented through coding, it essentially becomes machine learning. For instance, linear regression is fundamentally a statistical concept, but many people recognize it only through its implementation via libraries such as linear_model from sklearn.

In Biotech core philosophy of an explicit model was always at the heart of where neural networks were not taken up en mass long before their "epidemic emergence". The absence of a hypothesis in ML didn't sit well with scientific hypotheses. However, these hypotheses are also a hinderance because those models used radical different assumptions. For example, models of malaria transmission were frequently contradictory, but could equally fit the data, e.g. multiple strains or different rates of transmission. ML overcomes that limitation, there's no longer any need to assume anything and thats a huge advantage or tackling complexity.

Statistical analysis and machine learning diverge in their core objectives and methodologies while sharing a foundation in data. Statistical analysis delves into relationships and theories using sample data, relying on mathematical principles and probability to infer population insights. It scrutinizes reliability through p-values and confidence intervals. In contrast, machine learning prioritizes accurate predictions or decisions, embracing diverse algorithms to glean patterns autonomously from data, bypassing explicit programming. Here, performance reigns supreme over theoretical underpinnings.

Statistical analysis hinges on assumptions like normality or homoscedasticity, crucial for validity, particularly with smaller datasets. Emphasizing data distribution comprehension, it excels in elucidating insights within constrained contexts. Conversely, machine learning thrives on vast datasets, deftly discerning patterns amid complexity and noise. It boasts flexibility sans stringent data structure assumptions, enabling adaptability but potentially complicating model interpretability

Statistical analysis doesn't involve making assumptions arbitrarily. The assumption of normality, for instance, is rooted in the concept of the normal distribution, which suggests that most data points cluster around the average, with fewer observations in the tails. This isn't merely an assumption; it's a characteristic observed in many real-world datasets. As the volume of data increases, the assumptions about the data often reflect its actual properties more accurately. Breakdown: Normal Distribution: Explained that the normal distribution implies data clustering around the mean and tapering off at the extremes. Increased Data Volume: Noted that with more data, the assumptions often align closely with the reality of the dataset.

In biotech the model can be absolute because it's based on empirical wet-lab data resulting in universal behaviour. No matter how big the data set - the same universe applies. Machine learning excels where there isn't a model, the best example is protein folding/structure/antibody binding. DeepMind produced AlphaFold (CNN/covariance/big data). When models-exist via wet-lab data, graph theory, maximum likelihood, Bayes outperform ML e.g. for DNA mutation or 'RNA folding'. ML can be used to mimic an algorithm however for large datasets. I suspect the ML underperforms verses statistical models because of the small data set sizes.

Statistics, having evolved over centuries, prioritized simplicity due to the lack of computational resources available for solving complex problems. This led to the development of straightforward, optimized methods. In contrast, machine learning is a more recent field where people often rely on pre-written code libraries. This can lead to a situation where users apply these sophisticated models without a deep understanding of their internal workings, effectively treating them as "black boxes" whose processes are not fully transparent.

The reality is a classical statistic is run alongside the ML and the results "harmonised", usually by removing edge-effects between the two separate approaches. I don't personally see a conflict having spent some time developing solutions to obtain consistency between these methodological diverse approaches. The approaches support each other, on the one hand there's a clear model on the other a system that isn't fooled by assumptions that may not apply to the given data set.

In statistical analysis, simplicity reigns to understand how inputs impact outcomes, favoring clear, linear models for easy explanation. However, they falter with complex relationships. Machine learning dives into complexity, employing neural networks to grasp intricate patterns. While potent, these models act as "black boxes," obscuring prediction rationale. Striking a balance between simplicity and complexity is pivotal, with statistical analysis unraveling straightforward insights and machine learning navigating nuanced landscapes.

The statement overlooks a key aspect: predicting without understanding relationships between variables seems impractical. Even machine learning models, which excel at predictive tasks, must first undergo training to establish these relationships. Training a model involves discovering and quantifying the connections between variables, which is foundational to making accurate predictions.

Machine learning originated from the effort to automate statistical analysis through programming. Initially, validation techniques like mean absolute error (MAE) and accuracy were employed. With advancements in programming, more robust methods such as cross-validation emerged, strengthening validation processes. However, the core concept of ensuring model reliability has remained consistent.