In many surveys, studies may be carried out on large populations which may be geographically quite dispersed. To obtain the required number of subjects for the study by a simple random sample method will require large costs and will be cumbersome. In such cases, clusters may be identified (e.g. households) and random samples of clusters will be included in the study; then, every member of the cluster will also be part of the study. This introduces two types of variations in the data – between clusters and within clusters – and this will have to be taken into account when analyzing data. 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.

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

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. 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. 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.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.

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

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. Sometimes, a strictly random sample may be difficult to obtain and it may be more feasible to draw the required number of subjects in a series of stages. For example, suppose we wish to estimate the number of CATSCAN examinations made of all patients entering a hospital in a given month in the state of Maharashtra. It would be quite tedious to devise a scheme which would allow the total population of patients to be directly sampled. However, it would be easier to list the districts of the state of Maharashtra and randomly draw a sample of these districts. Within this sample of districts, all the hospitals would then be listed by name, and a random sample of these can be drawn. Within each of these hospitals, a sample of the patients entering in the given month could be chosen randomly for observation and recording. Thus, by stages, we draw the required sample. If indicated, we can introduce some element of stratification at some stage (urban/rural, gender, age).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). 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.

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

This book was probably the most lucidly written book that I have come across that explains Statistics to a person entirely alien to the field.Roundtree's book though is an absolute God send. It's helped me to understand the principles which lie at the heart of the statistics and what statistics can and can't show. It's not been an easy read. I have had to take it in small bite sized pieces, often re-reading sections to ensure I have what was said pinned down. I would still not claim to be comfortable with stats but I do now feel a little more comfortable with them.

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

A sample may be defined as random if every individual in the population being sampled has an equal likelihood of being included. Random sampling is the basis of all good sampling techniques and disallows any method of selection based on volunteering or the choice of groups of people known to be cooperative.[ 3] 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. 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. 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. I have a rather irregular history with statistics. After disliking maths GCSE but getting a very good mark, I avoided A-level maths like the plague. Upon arriving at university as a fresh-faced undergrad, I was disconcerted to discover that the first year of my social science degree included a compulsory statistics module. I passed that, then chose modules with no maths for the remaining two years. My dissertation was entirely qualitative. When I returned to studying as postgrad years later, I’d grudgingly come to accept that statistics are useful. My masters course included two statistics modules, which I appreciated the purpose of without enjoying. Then somehow, during the peculiar derangement of my PhD, I ended up teaching myself to use a fairly complex statistical methodology: multinomial logistic regression. The majority of my PhD research was quantitative. Now I find myself actually teaching statistics to undergrads. My 18 year old self would be amazed and horrified. It’s quite possible that I’m still outgrowing an ingrained dislike of maths that has much more to do with uninspired school teaching than the subject itself. In any case, I have a decent grasp of what stats are and why they’re useful, by social science standards.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 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. Stat อย่างผม อ่านแล้วอยากจะดึงคนเขียนมาจุ๊บด้วยความขอบคุณสักที เป็นสถิติแบบที่ใช้เรียนตอนป.ตรีเลย แต่อธิบายด้วยภาษาคน และการใส่ตัวอย่างมาแบบไม่มีกั๊ก ทำให้เนื้อหาหลายๆ อย่างที่ตอนเรียนเรารู้สึกว่า "ทำไมมันนามธรรมจังวะ? ตกลงไอ้ที่เรากำลังคำนวณกันอยู่นี่มันคืออะไร?" เคลียร์ขึ้นมาเยอะเลย 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. 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.