In high-dimensional statistics, you often encounter the so-called high-dimensional regime, where the number of variables or features ($p$) is much larger than the number of observations or samples ($n$). This is commonly written as $p \gg n$. Here, $n$ stands for the sample size, and $p$ represents the dimensionality, or the number of features being measured for each sample. In classical statistics, it is usually assumed that $n$ is much bigger than $p$, ensuring that there is enough data to reliably estimate model parameters. However, in many modern applications—such as genomics, image analysis, or finance—the number of features can be in the thousands or millions, while the number of samples remains limited. This shift to the high-dimensional regime fundamentally changes the behavior of statistical methods and requires new theoretical and practical approaches.