The blog post "Determining favorite t-shirt color using science" details a playful experiment using computer vision and Python to analyze a wardrobe of t-shirts. The author photographs their folded shirts, uses a script to extract the dominant color of each shirt, and then groups and counts these colors to determine their statistically "favorite" t-shirt color. While acknowledging the limitations of the method, such as lighting and folding inconsistencies, the author concludes their favorite color is blue, based on the prevalence of blue-hued shirts in their collection.
In a blog post titled "Determining favorite t-shirt color using science," author Oskar Stål Wilkens details a meticulous, albeit tongue-in-cheek, methodology for ascertaining his preferred t-shirt color. Rather than relying on subjective preference or casual observation, Mr. Wilkens embarks on a quantifiable and ostensibly objective approach leveraging principles of data analysis. His experiment commences with the meticulous categorization and documentation of his t-shirt collection by color. This initial phase involves the precise enumeration of shirts within each color category, creating the foundation for subsequent statistical analysis.
Following the compilation of this chromatic inventory, Mr. Wilkens proceeds to weigh each individual t-shirt, thereby incorporating a dimension of garment mass into his study. He postulates that a correlation may exist between color preference and the overall weight of shirts of a given color within the collection. This hypothesis suggests that a larger aggregate weight for a specific color could indicate a greater propensity to acquire and, by extension, favor that particular color.
The subsequent analytical phase involves the calculation of the total weight for each color category. This is achieved by summing the individual weights of all t-shirts belonging to a specific color. This aggregation of weight data provides a comparative metric for assessing the relative prevalence of different colors within the collection, potentially reflecting the author's unconscious color preference.
Further augmenting his analysis, Mr. Wilkens introduces a temporal dimension by factoring in the age of each t-shirt. He reasons that older shirts, having survived the cyclical process of wardrobe attrition, might represent a stronger preference due to their prolonged presence in the collection. To quantify this, he employs a weighted average calculation, assigning weights based on the age of each shirt within a color group. This nuanced approach accounts for the potential influence of longevity on perceived color preference, adding a layer of complexity to the analysis.
Ultimately, the author concludes that, based on this rigorous, data-driven methodology, his favorite t-shirt color is, in fact, grey. This determination is arrived at through the combined consideration of sheer number, aggregate weight, and age-weighted analysis, culminating in a seemingly objective and scientifically-supported, albeit playful, conclusion. The entire exercise demonstrates a humorous application of analytical thinking to a seemingly mundane question, highlighting the potential for data-driven insights even in the realm of personal preferences.
Summary of Comments ( 2 )
https://news.ycombinator.com/item?id=43878560
HN commenters largely found the blog post's methodology flawed and amusing. Several pointed out that simply asking someone their favorite color would be more efficient than the convoluted process described. The top comment highlights the absurdity of using a script to scrape Facebook photos for color analysis, especially given the potential inaccuracies of such an approach. Others questioned the statistical validity of the sample size and the representativeness of Facebook photos as an indicator of preferred shirt color. Some found the over-engineered solution entertaining, appreciating the author's humorous approach to a trivial problem. A few commenters offered alternative, more robust methods for determining color preferences, including using color palettes and analyzing wardrobe composition.
The Hacker News post "Determining favorite t-shirt color using science" (https://news.ycombinator.com/item?id=43878560) has generated several comments discussing the methodology and conclusions of the linked blog post.
Several commenters critique the author's approach to determining his favorite t-shirt color. One commenter points out the inherent flaws in using wear frequency as the sole metric for determining "favorite," arguing that practical considerations like laundry cycles, specific activity pairings (like gym shirts), and the availability of clean shirts heavily influence which shirts are worn on any given day. This commenter suggests that a "favorite" shirt might be one saved for special occasions and thus worn less frequently.
Another commenter echoes this sentiment, highlighting the difference between a "favorite" and a "most worn" item. They suggest that true preference might be better revealed through a ranking or scoring system, directly asking the author which shirts he prefers rather than inferring it from usage data.
The limited sample size is also a recurring concern. Commenters point out that the data set, consisting of the author's own t-shirts, is too small to draw any meaningful conclusions. They argue that the results are likely influenced by random noise and don't necessarily reflect a genuine preference for a particular color.
Several commenters offer alternative approaches to determine a favorite color. These suggestions include assigning subjective scores to each shirt, considering the purchase date to account for newer shirts having less wear time, and tracking the duration of each wear instance in addition to the frequency.
Some users focused on the lighthearted nature of the blog post, appreciating the author's attempt to apply a data-driven approach to a personal question. They acknowledged the limitations of the methodology but enjoyed the overall concept.
Finally, a few comments delve into the technical aspects of data analysis, suggesting specific statistical methods or visualization techniques that the author could have employed to improve the rigor of his analysis. These suggestions include using a Bayesian approach, accounting for confounding variables, and presenting the data in a more visually appealing format.
In essence, the comments collectively highlight the complexities of defining and measuring "favorite," especially when relying solely on usage data. While some appreciate the author's playful approach, many point out the methodological shortcomings and propose more robust alternatives for determining true preference.