Statistics without Tears: An Introduction for Non-Mathematicians

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The study population is the subset of the target population available for study (e.g. schizophrenics in the researcher's town).

Statistics without tears, a primer for non-mathematicians, Statistics without tears, a primer for non-mathematicians,

have a stats test tomorrow, revising the concepts actually made sense.. very grateful but we will see how it goes 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. 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. Only a short review here as others have written superbly on this book. I read this item cover to cover for a maths and algorithms university module and found it an excellent cornerstone to work on the rest of learning material. Like another reviewer here I've spent years running away from anything that looked remotely mathematical. Catchment areas depend on the demography of the area and the accessibility of the health center or hospital. Accessibility has three dimensions – physical, economic and social.[ 2] Physical accessibility is the time required to travel to the health center or medical facility. It depends on the topography of the area (e.g. hill and tribal areas with poor roads have problems of physical accessibility). Economic accessibility is the paying capacity of the people for services. Poverty may limit health seeking behavior if the person cannot afford the bus fare to the health center even if the health services may be free of charge. It may also involve absence from work which, for daily wage earners, is a major economic disincentive. Social factors such as caste, culture, language, etc. may adversely affect accessibility to health facility if the treating physician is not conversant with the local language and customs. In such situations, the patient may feel more comfortable with traditional healers.BOPA presents a 6 part live e-learning Statistics Webinar series to help you understand and work on Statistics without Tears! We’ve got a great speaker lined up with content that will be vital in your oncology pharmacy career. The sessions will be interactive and questions will be welcomed to help you with your statistical fears. We will run these on a monthly basis and the first one is free to all and then subsequently free to BOPA members. I'm not sure, whether this book is great, or it is just the cumulative result of many months of studying, but after reading it, I finally got the grasp on many basic things in statistics. There is the third option - I'm just too silly for statistics. 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.

Statistics without tears: Populations and samples - PMC Statistics without tears: Populations and samples - PMC

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. A brief and informative read that helped me review the statistics material I had studied, but I need to qualify that by saying this will not be enough. It's a good starting point, and if you've studied statistics before then it will remind you of the terms and help you conceptually. However, you will need to supplement this with other reading and practice centred around why you want to understand statistics and the tools you want to use. Aside from the mathematical complexities, I was plagued by programming languages that seem to have been designed by dinosaurs (I’m looking at you, “R”) and interaction with material that I thought I would have no relationship with following my graduation.Depending on the type of exposure being studied, there may or may not be a range of choice of cohort populations exposed to it who may form a larger population from which one has to select a study sample. For instance, if one is exploring association between occupational hazard such as job stress in health care workers in intensive care units (ICUs) and subsequent development of drug addiction, one has to, by the very nature of the research question, select health care workers working in ICUs. On the other hand, cause effect study for association between head injury and epilepsy offers a much wider range of possible cohorts.

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

Rowntree makes statistics more “human” by shedding away complicated statistical formulae and replacing them with robust conversations. He explores the concepts that these formulae describe, pausing throughout the book to ask questions that force you to think. This give-and-take approach made the book feel conversational, a momentous accomplishment in statistics in my view. This is a chance to finally make (more?) sense out of what you've learnt in school, especially regarding the estimation of a population via sampling (e.g. standard error), how significant a result is (e.g. z-test, t-test). To save this article to your Google Drive 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 Google Drive account. 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. A sample is any part of the fully defined population. A syringe full of blood drawn from the vein of a patient is a sample of all the blood in the patient's circulation at the moment. Similarly, 100 patients of schizophrenia in a clinical study is a sample of the population of schizophrenics, provided the sample is properly chosen and the inclusion and exclusion criteria are well defined.

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This article has been cited by the following publications. This list is generated based on data provided by Research workers in the early 19th century endeavored to survey entire populations. This feat was tedious, and the research work suffered accordingly. Current researchers work only with a small portion of the whole population (a sample) from which they draw inferences about the population from which the sample was drawn.