Scientific Discovery

I always loved products and technology. But ever since I was a child, I was especially fascinated by these big inventions, powered by transformative technological revolution that changed - everything! So I felt extremely lucky, when about 20 years ago, at the beginning of my career, I was just in time for one of these revolutions: when the Internet happened. Through the connected PC, the world we lived in has been transformed from a "physical world" -- where we used to go to places like libraries, and use things like encyclopedias and paper maps, to a "digital world" -- where we consume digital information and services from the convenience of our home. What was especially amazing, was the rate and scale of this transformation.

Hypothetical Testing is an application of your statistical model to the questions from the real world. In the hypothetical testing, you first assume the result as an assumption. It is called the null hypothesis. After the assumption, you hold an experiment for testing this hypothesis. Then after based on the results of the experiment.

Folklore has it that during the American Revolution, George Washington was approached by an enquiring member of the press who asked: "George! What keeps you up at night?" It wasn't the Continental Congress, who even then seemed challenged when it came to accomplishing anything. His reply: "Their Spies!" Since that time – more than 240 years – we've amassed insights as to the early indicators of trusted insiders inclining toward the dark side. Notwithstanding those gains, the best we've generally been able to do is catch the spies after they've already hurt us.

Hypothesis Testing, as such an important statistical technique applied widely in A/B testing for various business cases, has been relatively confusing to many people at the same time. This article aims to summarize the concept of a few key elements of hypothesis testing as well as how they impact the test results. The story starts from hypothesis. When we want to know any characteristics about a population like the form of distribution, the parameter of interest(mean, variance etc.), we make an assumption about it, which is called the hypothesis of population. Then we pull samples from population, and test whether the sample results make sense given the assumption.

"Scientific research has changed the world. Now it needs to change itself. There has been a growing concern about the validity of scientific findings. A multitude of journals, papers and reports have recognized the ever smaller number of replicable scientific studies. In 2016, one of the giants of scientific publishing, Nature, surveyed about 1,500 researchers across many different disciplines, asking for their stand on the status of reproducibility in their area of research. One of the many takeaways to the worrisome results of this survey is the following: 90% of the respondents agreed that there is a reproducibility crisis, and the overall top answer to boosting reproducibility was "better understanding of statistics". Indeed, many factors contributing to the explosion of irreproducible research stem from the neglect of the fact that statistics is no longer as static as it was in the first half of the 20th century, when statistical hypothesis testing came into prominence as a ...

Allen, associate professor of statistics, computer science and electrical and computer engineering at Rice and of pediatrics-neurology at Baylor College of Medicine, will address the topic in both a press briefing and a general session today at the 2019 Annual Meeting of the American Association for the Advancement of Science (AAAS). "The question is, 'Can we really trust the discoveries that are currently being made using machine-learning techniques applied to large data sets?'" "The answer in many situations is probably, 'Not without checking,' but work is underway on next-generation machine-learning systems that will assess the uncertainty and reproducibility of their predictions." Machine learning (ML) is a branch of statistics and computer science concerned with building computational systems that learn from data rather than following explicit instructions. Allen said much attention in the ML field has focused on developing predictive models that allow ML to make predictions about future data based on its understanding of data it has studied. "A lot of these techniques are designed to always make a prediction," she said.

Rice University statistician Genevera Allen says scientists must keep questioning the accuracy and reproducibility of scientific discoveries made by machine-learning techniques until researchers develop new computational systems that can critique themselves. Allen, associate professor of statistics, computer science and electrical and computer engineering at Rice and of pediatrics-neurology at Baylor College of Medicine, will address the topic in both a press briefing and a general session today at the 2019 Annual Meeting of the American Association for the Advancement of Science (AAAS). "The question is, 'Can we really trust the discoveries that are currently being made using machine-learning techniques applied to large data sets?'" "The answer in many situations is probably, 'Not without checking,' but work is underway on next-generation machine-learning systems that will assess the uncertainty and reproducibility of their predictions." Machine learning (ML) is a branch of statistics and computer science concerned with building computational systems that learn from data rather than following explicit instructions. Allen said much attention in the ML field has focused on developing predictive models that allow ML to make predictions about future data based on its understanding of data it has studied.

It offered'queue-less shopping' – a self-service concept that allowed consumers to shop the store by pushing a metal trolley around the aisles rather than waiting in line at a counter to be served. Today, retailers are becoming increasingly reliant on customer experience innovations such as this to ensure their continuity, as the industry is entering the most transformational period of its experience in response to the current crisis hitting the UK high street. Already a disruptor with its convenience-focused online retail service, Amazon redoubled its efforts to disrupt brick-and-mortar retail outlets by launching its own physical store, Amazon Go, in 2016. By and large, Amazon Go resembled any other supermarket: products on shelves, arranged by aisles; an assortment of baskets and trolleys for transporting goods; and a bright, fresh, welcoming atmosphere to attract customers. It's revolutionary move, however, was to use intelligent innovations in IoT technology to provide the most convenient shopping experience yet.

Since humans learned how to communicate, knowledge-sharing has been the core of our success, both as a species and as individuals. Around the campfire, after a successful hunt, our hunter-gatherer ancestors shared their stories of bravery and stories of misfortune too. By passing experience to one another, we've become more adept at surviving though times of hunger, famine and drought. Over time, multiple technologies and approaches have been invented, which increase the reach or the speed of knowledge transfer. Sometimes that improvement is so significant that it creates a paradigm shift, and we deem that to be a Knowledge Revolution.

But Inference, which bills itself as a "quarterly review of the sciences," was offering me a chance to write about a topic of my own choosing (subject to their approval). They also promised to pay me "appropriately" for my work, and the timing would have been great for book promotion. While I waited for an answer, I went to Inference's website. It looked like a real science publication -- featuring the original writing of scientists and other thinkers I respect, including MIT's Noam Chomsky and George Ellis at the University of Cape Town. There were 13 issues ranging back to 2014, covering a mix of subjects including physics, biology, and linguistics.