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. 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. 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 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.

Statistics Without Tears - Derek Rowntree PDF | PDF - Scribd Statistics Without Tears - Derek Rowntree PDF | PDF - Scribd

Rowntree says at the end If you feel I've raised more questions in your mind than I've answered, I shan't be surprised or apologetic. The library shelves groan with the weight of books in which you'll find answers to such questions (p185), although having said that to my eyes this is pretty comprehensive for a non-technical reader and the kinds of questions it has raised are not ones I require answers to. The book is clear and plainly explained with worked examples it is written in a seminar style - so the flow is interrupted by mini-questions. I was interested by one example which set out how by doing a single tailed analysis in a drugs trial you can potentially skew the presentation of the result to make a drug appear far more effective than it is ( Lies, damned lies and statistics afterall) As someone that has previously studied many of the covered topics, this was a comfortable way of reviewing and organising the subject matter. I found that some of the explanations provided were far more accessible than the way in which I was first taught statistics. have a stats test tomorrow, revising the concepts actually made sense.. very grateful but we will see how it goes 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. 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.Stat อย่างผม อ่านแล้วอยากจะดึงคนเขียนมาจุ๊บด้วยความขอบคุณสักที เป็นสถิติแบบที่ใช้เรียนตอนป.ตรีเลย แต่อธิบายด้วยภาษาคน และการใส่ตัวอย่างมาแบบไม่มีกั๊ก ทำให้เนื้อหาหลายๆ อย่างที่ตอนเรียนเรารู้สึกว่า "ทำไมมันนามธรรมจังวะ? ตกลงไอ้ที่เรากำลังคำนวณกันอยู่นี่มันคืออะไร?" เคลียร์ขึ้นมาเยอะเลย 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. 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. 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.

Statistics without Tears by Derek Rowntree | Waterstones Statistics without Tears by Derek Rowntree | Waterstones

An easy and useful read on the subject of Statistics. This is a book for the layman that (by its own definition) does not go into the mathematical equations as "there are enough books on that". What it does, however, is to introduce and explain the concepts in a way that can be easily digested. My one complaint would be that there are some errors in the few equations that it does use. By using this service, you agree that you will only keep content for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services

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Rowntree wants you to understand the concepts instead of the formulas, so it makes the read easier. If you ever had to do null-hypothesis testing in school decades ago, the content should come easy to you. 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. The study population is the subset of the target population available for study (e.g. schizophrenics in the researcher's town).