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As a digital detective, your mission is to decipher the preferences of your website visitors. Your primary tool? A/B testing – a method used in online controlled experiments where two versions of a webpage (version A and version B) are presented to different subsets of users under the same conditions. It’s akin to a magnifying glass, enabling you to scrutinize the minute details of user interactions across two versions of a webpage to discern their preferences. However, this case isn’t as straightforward as it seems. A recent article by Nicholas Larsen et al. in The American Statistician reveals the hidden challenges of A/B testing that can affect the results of online experiments. If these challenges aren’t tackled correctly, they can lead to misleading conclusions, affecting decisions in both online businesses and academic research.

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Census statistics play a pivotal role in making public policy decisions such as redrawing legislative districts and allocating federal funds as well as supporting social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, by adding noise to tabulated data and subsequently conducting postprocessing. The U.S. Census Bureau in particular has implemented a Disclosure Avoidance System (DAS) based on differential privacy technology to protect individual Census responses. This system adds random noise, guided by a privacy loss budget (denoted by ϵ), to Census tabulations, aiming to prevent the disclosure of personal information as mandated by law. The privacy loss budget value ϵ determines the level of privacy protection, with higher ϵ values allowing more noise. While the adoption of differential privacy has been controversial, this approach is crucial for maintaining data confidentiality. Other countries and organizations are also considering this technology as well. 

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Does a higher body mass index (BMI) increase the severity of COVID-19 symptoms? Mendelian randomization is one method that can be used to study this question without worrying about unmeasured variables (e.g., weight, height, or sex) that could affect the results. A recent paper published in the Annals of Statistics developed a new technique for Mendelian randomization which improves the ability to measure cause-and-effect relationships.

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Title:   An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study Authors & Year:   Hung T. Diep, Miron Kaufman, and Sanda Kaufman (2023) Journal: Entropy [DOI: https://doi.org/10.3390/e25070981] Review Prepared by Amal Machtalay Political polarization refers to a phenomenon where people’s political beliefs become radical, often resulting in the increasing division between political parties, which can have significant social consequences. This polarization is a complex system that is characterized by multiple factors: numerous interacting components (individual agents/voters, politicians, groups, media, etc.), non-linear dynamics (meaning that small changes can lead to large and uncertain effects), and emergent behavior (where collective phenomena result from local interactions, like when individuals engage with posts on social media that align with their political beliefs). The authors study the case of three USA political groups, each group indexed by $i\in  \left\{ 1,2,3\right\}$: Two types of interactions are classified and illustrated in Figure 1:…

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By: Erin McGee  Paper title: Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans Authors and year: Cory McCartan, Kosuke Imai, 2023  Journal: Annals of Applied Statistics (Forthcoming 2023),  https://doi.org/10.48550/arXiv.2008.06131 In 2011, the Pennsylvania General Assembly was accused of drawing a redistricting plan for the state that diluted the power of Democratic voters, while strengthening the Republican vote. The case made its way to the Pennsylvania Supreme Court, where it was determined to be an unfairly drawn map.  With more complicated techniques, gerrymandering, or altering districts to purposefully amplify the voting power of some, while reducing others, becomes harder to recognize. Gerrymandered districts are usually identifiable by the ‘jigsaw’ shapes that split counties and municipalities in an attempt to pack certain voting groups into the same district, while splitting others. However, proving that a district map has been purposefully manipulated, as it was in Pennsylvania, is no easy task.…

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In order to validate our understanding of the world around us, we want to compare theoretical models to data we have actually observed. Often, these models are functions of parameters, and we want to know the values of those parameters such that the models most closely represent the world. For example, we may believe the concentration of one molecule in a chemical reaction should decrease exponentially with time. However, we also want to know the rate constant, the parameter in the model that multiplies time in the exponential, such that  the model exponential curve actually resembles a specific reaction that we observe. This is the problem of parameter inference, for which we often turn to Bayesian methods, especially when working with complex models and/or many parameters..

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Title:   Assurance for Sample Size Determination in Reliability Demonstration Testing Authors & Year:   Kevin Wilson & Malcolm Farrow (2021) Journal:   Technometrics [DOI: 10.1080/00401706.2020.1867646] Why Reliability Demonstration Testing? Ensuring high reliability is critical for hardware products, especially those involved in safety-critical functions such as railway systems and nuclear power reactors. To build trust, manufacturers use reliability demonstration tests (RDT) where a sample of products is tested and failures are observed. If the test meets specific criteria, it demonstrates the product’s reliability. The RDT design varies based on the type of hardware product being tested, whether it is failure on demand or time to failure. Traditionally, sample sizes for RDT have been determined using methods that consider the power of a hypothesis test or risk criteria. Various approaches, such as Bayesian methods and risk criteria evaluation, have been developed over the decades in order to enhance the effectiveness of RDT. These measures…

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The setting repeats depressingly often. A hurricane inching towards the Florida coast. Weather scientists glued to tracking monitors, hunched over simulation printouts, trying to remove people out of harm’s way. Urgent to them is the need to mark a patch of the shore where the hurricane is likely to hit. Those living in this patch need to be relocated. These scientists, and many before them – it’s hard to say since when – realized what’s at issue here is not quite so much the precise location the storm is going to hit – precise to the exact grain of sand, but a stretch of land (whose length may shrink gradually depending on how late we leave the forecasting) where it is going to affect people with a high chance.  A forecast interval of sorts.

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The meteorologists of today no longer ask themselves, “Will it rain tomorrow?”, but rather, “What is the probability it will rain tomorrow?”. In other words, weather forecasting has evolved beyond giving simple point projections, and instead has largely shifted to probabilistic predictions, where forecast uncertainty is quantified through quantiles or entire probability distributions. Probabilistic forecasting was also the subject of my previous blog post, where the article of discussion explored the intricacies of proper scoring rules, metrics that allow us to compare and rank these more complex distributional forecasts. In this blog post, we explore facets of an even more basic consideration: how can one be sure their probabilistic forecasts make sense and actually align with the data that ended up being observed? This ‘alignment’ between forecasted probabilities and observations is referred to as probabilistic calibration. Put more concretely, when a precipitation forecasting model gives an 80% chance of rain, one would expect to see rain in approximately 80% of those cases (if the model is calibrated).

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Machine learning models are excellent at discovering patterns in data to make predictions. However, their insights are limited to the input data itself. What if we could provide additional knowledge about the model features to improve learning? For example, suppose we have prior knowledge that certain features are more important than others in predicting the target variable. Researchers have developed a new method called the feature-weighted elastic net (“fwelnet”) that integrates this extra feature knowledge to train smarter models, resulting in more accurate predictions than regular techniques.

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