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Statistics without Tears: An Introduction for Non-Mathematicians

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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. 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. 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. Ascertainment of a particular disease within a particular area may be incomplete either because some patient may seek treatment elsewhere or some patients do not seek treatment at all. Focus group discussions (qualitative study) with local people, especially those residing away from the health center, may give an indication whether serious underreporting is occurring.

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

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 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.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. 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. 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! - BOPA Statistics without Tears! - BOPA

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. 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. 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.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. Speaker: Sian Williams is a Senior Lecturer in Health Psychology and Pharmacy Practice at the University of Brighton. She has over 20 years experience of teaching statistics to undergraduates and postgraduates in a range of health professions and with a range of experience (and levels of statistics-phobia!). 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 A Primer For Non Mathematicians ( 1981) Statistics Without Tears A Primer For Non Mathematicians ( 1981)

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. 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. 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. 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). The focus of this book is on the 'why' of performing statistical calculations so that the 'how' of those same calculations makes sense.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)

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

Stat อย่างผม อ่านแล้วอยากจะดึงคนเขียนมาจุ๊บด้วยความขอบคุณสักที เป็นสถิติแบบที่ใช้เรียนตอนป.ตรีเลย แต่อธิบายด้วยภาษาคน และการใส่ตัวอย่างมาแบบไม่มีกั๊ก ทำให้เนื้อหาหลายๆ อย่างที่ตอนเรียนเรารู้สึกว่า "ทำไมมันนามธรรมจังวะ? ตกลงไอ้ที่เรากำลังคำนวณกันอยู่นี่มันคืออะไร?" เคลียร์ขึ้นมาเยอะเลยA 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. 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. As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with,

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