Scientifically Speaking

***BUSINESS BOOK AWARDS 2022 SHORTLISTED TITLE*** Now more than ever, the scientific and medical community is under the microscope and in front of the media. Science matters, and in a post-truth world it’s more important than ever for scientists and physicians to be heard. But there’s a challenge. To get people to listen, you can’t communicate in writing alone anymore. You need to speak up and be seen - on stage, online, and on camera. To do this well you need to master the art of influential speaking, which is something you weren’t taught at university or medical school. This book teaches you how to become a compelling scientific speaker so that you can put your message across with confidence and clarity, both online and in person. It’s written by a speaking coach with 25 years of experience in science communications. Part One shows how speaking can help you to win the war of attention, benefiting both your field and your career. Part Two explains how to craft your scientific message in a way that connects with your audience and achieves your goal. Including how to be memorable, handle the Q&A, and communicate risk. Part Three gives you a tool kit for speaking with energy and conviction in all types of situations. These include virtual channels, which are particularly important in the post-COVID era. Jo Browning is the Founder and Managing Director of Filshie Browning Associates, and its Principle Consultant. She has 25 years of experience in verbal communication skills, and helps scientists and physicians to improve their content, competence, and confidence, so that they can communicate with impact and authority. This enables them to enhance their reputations and build more effective relationships with both their peers and others.

In these days of ever-increasing specialization, it is important to gain a broad appreciation of science. Entertaining and informative, Scientifically Speaking: A Dictionary of Quotations, Second Edition contains the words and wisdom of several hundred scientists, writers, philosophers, poets, and academics. The largest compilation of published science quotations available, the book presents quotations that give depth and breadth to science, and provides the visions and styles of scientists past and present. The bibliography is useful if you want more details about the quotations listed. The extensive author and subject indexes provide the perfect tool for locating quotations for practical use or pleasure. This book can be read for entertainment or used as a handy reference.

"Speaking About Science : A Manual for Creating Clear Presentations is essential reading for anyone who presents data at meetings and conferences. Based on the curriculum that authors have developed for their public speaking courses, the book provides the practical tools all speakers need to create clear and compelling presentations for any audience."--BOOK JACKET.

Statistically Speaking is a book of quotations. It brings together the best expressed thoughts that are especially illuminating and pertinent to the disciplines of probability and statistics. The book is an aid for the individual who loves to quote – and to quote correctly.

In these days of ever-increasing specialization, it is important to gain a broad appreciation of science. Entertaining and informative, Scientifically Speaking: A Dictionary of Quotations, Second Edition contains the words and wisdom of several hundred scientists, writers, philosophers, poets, and academics. The largest compilation of published science quotations available, the book presents quotations that give depth and breadth to science, and provides the visions and styles of scientists past and present. The bibliography is useful if you want more details about the quotations listed. The extensive author and subject indexes provide the perfect tool for locating quotations for practical use or pleasure. This book can be read for entertainment or used as a handy reference.

While the connections between science fiction and race have largely been neglected by scholars, racial identity is a key element of the subjectivity constructed in American SF. In his Mars series, Edgar Rice Burroughs primarily supported essentialist constructions of racial identity, but also included a few elements of racial egalitarianism. Writing in the 1930s, George S. Schuyler revised Burroughs' normative SF triangle of white author, white audience, and white protagonist and promoted an individualistic, highly variable concept of race instead. While both Burroughs and Schuyler wrote SF focusing on racial identity, the largely separate genres of science fiction and African American literature prevented the similarities between the two authors from being adequately acknowledged and explored. Beginning in the 1960s, Samuel R. Delany more fully joined SF and African American literature. Delany expands on Schuyler's racial constructionist approach to identity, including gender and sexuality in addition to race. Critically intertwining the genres of SF and African American literature allows a critique of the racism in the science fiction and a more accurate and positive portrayal of the scientific connections in the African American literature. Connecting the popular fiction of Burroughs, the controversial career of Schuyler, and the postmodern texts of Delany illuminates a gradual change from a stable, essentialist construction of racial identity at the turn of the century to the variable, social construction of poststructuralist subjectivity today.

Statistical models attempt to describe and quantify relationships between variables. In the models presented in this chapter, there is a response variable (sometimes called dependent variable) and at least one predictor variable (sometimes called independent or explanatory variable). When investigating a possible cause-and-effect type of relationship, the response variable is the putative effect and the predictors are the hypothesized causes. Typically, there is a main predictor variable of interest; other predictors in the model are called covariates. Unknown covariates or other independent variables not controlled in an experiment or analysis can affect the dependent or outcome variable and mislead the conclusions made from the inquiry (Bock, Velleman, & De Veaux, 2009). A p value (p) measures the statistical significance of the observed relationship; given the model, p is the probability that a relationship is seen by mere chance. The smaller the p value, the more confident we can be that the pattern seen in the data 2 is not random. In the type of models examined here, the R measures the prop- tion of the variation in the response variable that is explained by the predictors 2 specified in the model; if R is close to 1, then almost all the variation in the response variable has been explained. This measure is also known as the multiple correlation coefficient. Statistical studies can be grouped into two types: experimental and observational.