Statistics without Tears: An Introduction for Non-Mathematicians

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To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. the possibility of bias in samples, the distinction between significance and importance, the fact that correlation does not imply causation, and that people sometimes simply get things wrong.' This is an excellent introduction to statistical thinking. The language used is conversational and easy to understand as you are guided through examples and ways of thinking about statistics. The focus of this book is on the 'why' of performing statistical calculations so that the 'how' of those same calculations makes sense.

Statistics Without Tears: An Introduction for Non-Mathematicians Statistics Without Tears: An Introduction for Non-Mathematicians

When generalizing from observations made on a sample to a larger population, certain issues will dictate judgment. For example, generalizing from observations made on the mental health status of a sample of lawyers in Delhi to the mental health status of all lawyers in Delhi is a formalized procedure, in so far as the errors (sampling or random) which this may hazard can, to some extent, be calculated in advance. However, if we attempt to generalize further, for instance, about the mental statuses of all lawyers in the country as a whole, we hazard further pitfalls which cannot be specified in advance. We do not know to what extent the study sample and population of Delhi is typical of the larger population – that of the whole country – to which it belongs. In retrospect, these appear to be mistakes. As an aspiring trader, my world is deeply tied to statistics and programming languages (although I still think “R” is ugly). Reading “Statistics Without Tears” slowly chipped away at my prejudice toward the subject. Derek Rowntree writes and educates in a way that I believe most statistics teachers can only dream of doing. Instead of dosing off during the book’s “lectures,” like I did in university ones (on the ones I didn’t skip), this book had me hooked from beginning to end. If cases of a disease are being ascertained through their attendance at a hospital outpatient department (OPD), rather than by field surveys in the community, it will be necessary to define the population according to the so-called catchment area of the hospital OPD. For administrative purposes, a dispensary, health center or hospital is usually considered to serve a population within a defined geographic area. But these catchment areas may only represent in a crude manner with the actual use of medical facilities by the local people. For example, in OPD study of psychiatric illnesses in a particular hospital with a defined catchment area, many people with psychiatric illnesses may not visit the particular OPD and may seek treatment from traditional healers or religious leaders. That being said there were some good tidbits of information on misuse of statistics by third parties to strengthen their cause. Biases in marketing is a topic that interests me since I am obviously a consumer myself.Eh, it was ok. I'm not sure why these books seem to be so against updating to show use cases with current computational software (R, Python,...even...ugh, Excel), but they do seem to cavil at the idea of it. That would be fine, as I read this book looking for any little intuitions that I may have missed about some basic topics, but unfortunately, both the intuitions and the theoretical portions felt half finished. If you're looking for a refresher on statistics that helps with intuitions, I would definitely go with Head First Statistics over this one. Essentially, the book covers all the statistics in A Level Maths (and bits of Further Stats), explaining it in an accessible way and actively encourages you to think (so there really is no escape). The hatred of crunching numbers and learning methods without understanding what I was doing has now been rectified. So why read this book? Because the undergrads I taught this term, and probably the postgrads I’ll teach next term, appear petrified and confused by quantitative methods. It’s so difficult to tell whether students are really grasping the concepts you explain in lectures, particularly when there’s no exam to test comprehension. These are social science students and their prior exposure to stats seems to have been minimal. When I spotted this book in library, I wondered if it could help me to explain the basics more clearly. And I think it just might. I found it very easy to follow and a helpful reminder. Rowntree’s explanation of the difference between parametric and non-parametric tests is especially lucid and useful. That said, I doubt I'll have time to include such careful and painstaking explanations in my lectures. I’ll definitely recommend the book to students, though. It’s not at all fashionable to suggest students read entire books, but honestly I think this one is much better than an explanatory video, the more trendy teaching medium. Regardless, this should be the first book anyone should read if they want an introduction to the world of statistics. It contains no calculations and it is very engaging.

Statistics without Tears: An Introduction for Non-Mathematicians Statistics without Tears: An Introduction for Non-Mathematicians

A population is a complete set of people with a specialized set of characteristics, and a sample is a subset of the population. The usual criteria we use in defining population are geographic, for example, “the population of Uttar Pradesh”. In medical research, the criteria for population may be clinical, demographic and time related.In statistics, a population is an entire group about which some information is required to be ascertained. A statistical population need not consist only of people. We can have population of heights, weights, BMIs, hemoglobin levels, events, outcomes, so long as the population is well defined with explicit inclusion and exclusion criteria. In selecting a population for study, the research question or purpose of the study will suggest a suitable definition of the population to be studied, in terms of location and restriction to a particular age group, sex or occupation. The population must be fully defined so that those to be included and excluded are clearly spelt out (inclusion and exclusion criteria). For example, if we say that our study populations are all lawyers in Delhi, we should state whether those lawyers are included who have retired, are working part-time, or non-practicing, or those who have left the city but still registered at Delhi. Please list any fees and grants from, employment by, consultancy for, shared ownership in or any close relationship with, at any time over the preceding 36 months, any organisation whose interests may be affected by the publication of the response. Please also list any non-financial associations or interests (personal, professional, political, institutional, religious or other) that a reasonable reader would want to know about in relation to the submitted work. This pertains to all the authors of the piece, their spouses or partners.