Major Religions of the World Ranked by Number of Adherents

Major Religions of the World Ranked by Number of Adherents

• Christianity: 2.1 billion
• Islam: 1.5 billion
• Secular/Nonreligious/Agnostic/Atheist: 1.1 billion
• Hinduism: 900 million
• Chinese traditional religion: 394 million
• Buddhism: 376 million
• primal-indigenous: 300 million
• African Traditional & Diasporic: 100 million
• Sikhism: 23 million
• Juche: 19 million
• Spiritism: 15 million
• Judaism: 14 million
• Baha'i: 7 million
• Jainism: 4.2 million
• Shinto: 4 million
• Cao Dai: 4 million
• Zoroastrianism: 2.6 million
• Tenrikyo: 2 million
• Neo-Paganism: 1 million
• Unitarian-Universalism: 800 thousand
• Rastafarianism: 600 thousand
• Scientology: 500 thousand

This listing is not a comprehensive list of all religions, only the "major" ones . There are distinct religions other than the ones listed above. But this list accounts for the religions of over 98% of the world's population.

How are adherents counted?
There are five main methods for determining the number of adherents in a faith group:
1. Organizational reporting: Religious bodies (such as churches or denominations) are asked how many adherents or members they have. This is the simplest and least expensive method, but it can be highly unreliable. Different faith groups measure membership differently. Some count as members only those who are actively attending services or who have passed through a lengthy initiation process. Others groups count all who have been baptized as infants and are thus on the church records, even though some of those people may have joined other faith groups as adults. Some groups over-report membership and others under-report membership. When asked what religion they consider themselves to be a part of, many may name a religion that does not have them on their rolls. In the United States, for instance, three times as many people claim to be Unitarian Universalists than are actually on church records.

2. Census records: Many countries periodically conduct a comprehensive household-by-household census. Religious preference is often a question included in these census counts. This is a highly reliable method for determining the religious self-identification of a given population. But censuses are usually conducted infrequently. The latest census may be too old to indicate recent trends in religious membership. Also, many countries either have no accurate census data, or do not include questions regarding religious affiliation. It has been over fifty years since the United States included such a question in its national census, but Canada, India, New Zealand, Australia and other countries have very thorough, recent census data on the topic.

3. Polls and Surveys: Statistical sampling using surveys and polls are used to determine affiliation based on religious self-identification. The accuracy of these surveys depends largely on the quality of the study and especially the size of the sample population. Rarely are statistical surveys of religious affiliation done with large enough sample sizes to accurately count the adherents of small minority religious groups.

4. Estimates based on indirect data: Many adherent counts are only obtained by estimates based on indirect data rather than direct questioning or directly from membership roles. Wiccan groups have traditionally been secretive and often their numbers can only be estimated based on magazine circulations, attendance at conferences, etc. The counts of many ethnic-based faith groups such as tribal religions are generally based on the size of associated ethnic groups. Adherents of some tribal religions (such as Yoruba) are sometimes counted simply by counting the members of the tribe and assuming everybody in it is an adherent of the religion. Counts of Eastern Orthodox religious bodies are often done the same way. Such estimates may be highly unreliable.

5. Field work: To count some small groups, or to count the number of adherents a larger group has within a specific geographical area, researchers sometimes do "field work" to count adherents. This is often the only way to count members of small tribal groups or semi-secretive, publicity-shy sects. Field work may involve contacting leaders of individual congregations, temples, etc., conducting interviews with adherents, counting living within enclaves of the group, or counting those participating in key activities. There is substantial overlap between "estimates" and "field work."

14 Exit Poll Statistics About Obama’s Victory

14 Exit Poll Statistics About Obama’s Victory

There’s not much more that I can say that others have not said already regarding the significance of Barack Obama’s election as our next President: historic, monumental, amazing, inspiring, emotional, and quite simple, awesome. As a sociologist and demographer, I’d like to offer a few statistics on his election to be our next President:
• 136.6 million Americans voted, representing a 64.1% turnout rate, the highest since 65.7 percent in 1908.
• Obama is the first Democrat to receive more than 50 percent of the popular vote since Jimmy Carter in 1976.
From CNN’s exit poll tabulations:
• Obama received 49% of all the male votes (vs. 48% for McCain) and 56% of the female votes (vs. 43% for McCain). But once you break it down by race, Obama only received 41% of the White male vote (vs. 57% for McCain) and 46% of the White female vote (vs. 53% for McCain).
• 95% of African Americans, 66% of Latinos, and 61% of Asian Americans voted for Obama. Along with the previous statistic, what this tells us is that while large numbers of Whites supported Obama, ultimately non-Whites helped put him over the top.
• 66% of voters under the age of 30 voted for Obama.
• 52% of voters making $200,000 or more voted for Obama (vs. 46% for McCain).
• By level of education, the groups that voted for Obama the most were those at both ends of the spectrum — those who have no high school degree and those with a postgraduate degree.
• 54% of Catholics voted for Obama (vs. 45% for McCain), although among White Catholics, 47% voted for Obama while 52% for McCain.
• 50% of voters living in the suburbs voted for Obama (vs. 48% for McCain).
• Among voters who felt that their taxes would go up if Obama were elected President, 43% still voted for him.
• 64% of all voters felt that McCain unfairly attacked Obama, while only 49% of all voters felt Obama unfairly attacked McCain.
• 47% of all voters felt that, regardless of who is President, race relations are likely to get better in the next few years, and of those, 70% voted for Obama. In contrast, 15% felt that race relations are likely to get worse and of those, 70% voted for McCain.
• 9% of voters said that the candidate’s race was an important factor and of those, 53% voted for Obama.
• 58% of voters said that issues, rather than personal qualities, were more important to them and of those, 60% voted for Obama. In contrast, 59% of those who believed personal qualities were more important to them voted for McCain.
For me, the most telling and interesting of these statistics is first, that shows 52% of voters making at least $200,000 voted for Obama versus 46% voting for McCain. In my opinion, that is pretty astounding — those in the upper 6%-7% of the nation in terms of wealth supported Obama more than McCain, even though their taxes are likely to go up slightly. I give these voters a lot of credit for supporting Obama and goes a long way to counteract the stereotype of them as caring only about their wallets.
But perhaps the most significant statistic is how Obama captured almost all of the African American votes and a huge percentage of the Latino and Asian American votes and how, most likely, this was likely a big factor in helping to put him over the top.
It is certainly true that White votes still outnumbered non-White votes for Obama and that in the end, the scope of Obama’s victory shows that he has significant, broad-based support from Americans of all racial backgrounds. Nonetheless, I think it’s pretty clear that the Latinos and Asian Americans did constitute a crucial “swing vote” and ultimately, they overwhelmingly rallied to Obama’s support.
While observers, commentators, and scholars will debate this particular issue for the foreseeable future, it does appear that, combined with their continuing population growth, Latino and Asian American voters are poised to have this kind of potential impact and power for years to come.

SCHEDULE

Sept 16 - Verb Tenses
Sept 21 - text discussion at the bog (there is no class)
Sept 23 - text discussion at the bog (there is no class)
Sept 28 - Nominal Groups
Sept 30 - Nominal Groups

Oct 5 - text discussion at the bog (there is no class)
Oct 7 - Word Formation
Oct 14 - text discussion at the bog (there is no class)
Oct 19 - Text Markers
Oct 21 - Dictionary Use
Oct 26 - text discussion at the bog (there is no class)
Oct 28 - On-line Questionnaire (there is no class)

Nov 09 - Presentation Guidelines + General Review
Nov 11- General Review
Nov 16 - Presentation Guidelines
Nov 18 - Presentation Guidelines
Nov 23 - Presentations
Nov 25 - Presentations
Nov 30 - Presentations

Dez 02 - Presentations
Dez 07 - Presentation Feedback
Dez 09 - Last class

TEXT DISCUSSION GUIDELINES

Questions for text discussion at the blog

You may choose two of the following questions to answer/discuss. You can comment on a colleague’s response (as your contribution), but you must not use the same ideas.

1. Which were the methods of data collection and analysis? Which are the elements in the text that support your answer? In case they are not explicit in the text, please conduct a research, infer and justify your answer (conclusions).

2. What is the importance of statistics to:
A. the science that the text refers to?
B. the society as a whole?

3. What is the statistician's role in the accomplishment of the study reported in the text?

4. What new information have you learned from the text as:
A. a citizen?
B. a professional (statistician)?

5. Based on your background knowledge, what information could you add to the text? Why?

6. What is your opinion about the information presented in the text? Give reasons to support your idea.

The importance of forensic statistics
Forensic statistics is the application of statistics to forensic science and the law.
Broadly speaking, forensic science is the analysis of traces of evidence (such as body fluids, glass fragments, footprints and drugs) left at the scene of a crime by the criminal, victim or others. This evidence may be used subsequently to either implicate or exonerate a person suspected of committing that crime, or just to gain further insight into the incident. Over the years, with increasing technological advancement, forensic science has become a key part of criminal investigations worldwide.
But forensic science doesn't just involve identifying traces of evidence – sometimes it isn't obvious just what a piece of evidence really is. Other important questions that need to be answered are just how the evidence came to be at the crime scene, where did it originally come from, and who left it there. This suggests a natural role for statistics, as these questions can typically only be answered in terms of probabilities. So it is not surprising that the primary task of forensic statisticians is to evaluate any evidence found at a crime scene, so that this evidence can be appropriately presented to a jury in court. This task obviously carries great responsibility.
The advent of DNA profiling in the 1980s brought a big change in the way the legal system viewed quantitative data. Now a quantitative approach is being requested in many areas, far removed from the original area of DNA profiling. The earlier research and development work is being applied and further work is being done to tackle the increasingly more complex cases which arise in bringing a sound statistical approach to the assessment of evidence.

What does this career entail?
Please note first that forensic statisticians can operate under various guises (further details can be found in the "Who employs forensic statisticians?" section). At one end of the scale, there are people employed by forensic science units specifically to analyse forensic data; at the other end, there are some university lecturers who specialise in carrying out statistical research on forensic matters and act as consultant forensic statisticians when required. The methods of statistical analysis used will usually be similar, no matter where on this scale a forensic statistician is operating.
For an appropriate evaluation of evidence, a comparison of probabilities of the evidence under two different propositions is required. These propositions are usually those put forward by the prosecution and the defence. There are advanced statistical methods for doing this (for readers who are technically inclined, they are based on likelihood ratios or Bayes' factors). Much theoretical work has been done in the development of these methods. Calculations based on them might sometimes be fairly straightforward, though it also often turns out that there are non-standard issues to consider.
One example of casework that a forensic statistician may be involved with is DNA profiling, which is a powerful method of identification using genetics. Often, the evidence to be evaluated involves human (or sometimes animal) biological material such as blood, semen or vaginal fluid. Considerable work has been done in statistical and population genetics in assessing the importance of such evidence. Applications, however, are often not restricted to simple cases with one sample of DNA left at the scene of a crime and one suspect. Complications very often arise, for example because relatives may be involved, or the suspect may have been identified by a search through a DNA profile database, or the sample found at the crime scene may be a mixture of body fluids from more than one person. More advanced statistical methods are required in such situations.
Another role of a forensic statistician relates to sampling problems and determination of sample size. In some cases, it is necessary to examine a consignment of similar-looking items, and it is often not practical to examine every item. This may be purely on financial grounds but may be on health grounds also. The question then arises as to how many items should be examined on a sampling basis. For example, the consignment to be examined may be a set of CDs, some of which are thought to contain pornographic material. Then it is desirable for the examining officers to examine as few CDs as is commensurate with a good description of the proportion of the CDs which are illicit. The sample size determination is really just a quality control problem; there are UN Guidelines where the problem concerns drugs.
Finally, an important part of being a forensic statistician, as indeed it is for any statistician, is the ability to communicate results effectively to non-statisticians. Forensic statisticians are often required to attend court cases as "expert witnesses". This involves reporting calculated probabilities, or other statistical measures, to the jury, and explaining to them how the calculations were performed. This is a challenge in itself, as the jury will typically consist of people who have little knowledge of statistical methods, and is further complicated by the need to choose careful wording (so as not to "lead" the jury into a decision on guilt or innocence of a defendant).

Who employs forensic statisticians?
There is an increasing need for people to understand the role and application of probability and statistics in forensic science and the law. However, there are essentially no jobs and no career structure in forensic statistics in the UK as such.
The Forensic Science Service (FSS) of England and Wales has an Interpretation Group which considers problems of evidence evaluation, but it is small in size. In many cases, if the FSS wants help with a problem, it employs consultants. The Home Office has a Policing and Reducing Crime Unit which offers occasional contract work for statisticians to assist in particular projects. Individual police forces and law firms may also seek assistance with particular cases.
The area of DNA profiling is also growing. Although not strictly forensic statistics, there may be opportunities for statisticians in companies specialising in the analysis of DNA profiles for paternity and kinship testing.
The main route into a career involving forensic statistics is essentially as an academic, either as a university lecturer or specifically as a researcher. You would probably need to gain a lecturing or research post in a mathematics or statistics university department, and then pursue a research or consultancy path as part of your day-to-day work there. There are some research institutes that primarily focus on forensic science and statistics, notably the Joseph Bell Centre for Forensic Statistics and Legal Reasoning

What qualifications are typically required?
As we have explained above, it is often necessary to follow a career as an academic in order to be involved with forensic statistics, and the qualifications required for this are outlined on our career as a university lecturer page. For other positions, within forensic science units (UK-based or otherwise) or companies, an appropriate undergraduate degree is likely to be a minimum requirement, with postgraduate experience an advantage.
Continuing professional development
Forensic statisticians need to continue their personal and professional development. This can be done in several ways.
Most universities offer staff development programmes in which you may take short courses on almost anything, including computing software, presentational skills, management development and teaching skills. Even if you do not work in a university, at least some courses of a similar nature are likely to be available.
The statistical methods on which your work is based are also, of course, used in other application areas. You will probably find that there are conferences where the latest developments in these methods are explored; you might be able to submit papers or abstracts about your statistical work and attend the conferences, when you could have the opportunity to present your own papers as well as attend other presentations. Many conferences also have workshops in which you could participate. You are also likely to be encouraged to write up the statistical aspects of your work as formal papers for academic journals.
It will be extremely important to develop your communication skills, so that you can report your findings effectively to members of the police and legal professions, and all the more so if you will actually be appearing in court as an expert witness. It will also be important to gain some understanding of the legal system.
Your professional work as a statistician might well make it appropriate for you to seek the professional qualification of Chartered Statistician (CStat), which would give you a professional affiliation with the Royal Statistical Society.

Salaries and opportunities for advancement
As we have explained, work as a forensic statistician is likely to be on a consultancy basis, often coupled with a career as an academic. So there is, in effect, no salary structure attached to a career as a forensic statistician per se. General information about salaries in the academic sector can be found on our career as a university lecturer page. This also contains information about academic career structures and promotion prospects.

How to locate job vacancies for forensic statisticians
Again, the absence of a career as such means that there are unlikely to be advertisements explicitly for "forensic statisticians" as such, though there may be opportunities for statisticians and programmers within government or commercial forensic science and DNA profiling organisations. Any advertisements might appear in several places, including the following:
Daily newspapers (The Times, The Guardian and The Independent are probably the best).
The newsletter RSS NEWS of the Royal Statistical Society which is issued monthly to all members of the Society.
Electronic mailing lists (such as Allstat).
New Scientist.

Recycling Non-Organic Waste
Aka Municipal Solid Waste (MSW)
MSW consists of everyday items such as product packaging, grass clippings, furniture, clothing, bottles, food scraps, newspapers, appliances, paint, and batteries.

In 2006, the US produced more than 251 million tons of MSW, which is approximately 4.6 pounds of waste per person per day. That is a lot of waste Several MSW management practices, such as source reduction, recycling, and composting, prevent or divert materials from the wastestream.
Source reduction involves altering the design, manufacture, or use of products and materials to reduce the amount and toxicity of what gets thrown away.
Recycling diverts items, such as paper, glass, plastic, and metals, from the wastestream. These materials are sorted, collected, and processed and then manufactured, sold, and bought as new products.
Composting decomposes organic waste, such as food scraps and yard trimmings, with microorganisms (mainly bacteria and fungi), producing a humus-like substance.

Other practices address those materials that require disposal. Landfills are engineered areas where waste is placed into the land. Landfills usually have liner systems and other safeguards to prevent groundwater contamination. Combustion is another MSW practice that has helped reduce the amount of landfill space needed. Combustion facilities burn MSW at a high temperature, reducing waste volume and generating electricity.
How should we deal with MSW?
EPA has ranked the most environmentally sound strategies for MSW.
Source reduction (including reuse)
Recycling and composting, and, lastly,
Disposal in combustion facilities and landfills.
Currently, in the US, 32.5% is recovered and recycled or composted, 12.5 percent is burned at combustion facilities, and the remaining 55 percent is disposed of in landfills.
Source Reduction (Waste Prevention)
Source reduction can be a successful method of reducing waste generation. Practices such as grasscycling, backyard composting, two-sided copying of paper, and transport packaging reduction by industry have yielded substantial benefits through source reduction.
Source reduction has many environmental benefits. It prevents emissions of many greenhouse gases, reduces pollutants, saves energy, conserves resources, and reduces the need for new landfills and combustors.
Recycling

Recycling, including composting, diverted 82 million tons of material away from disposal in 2006, up from 15 million tons in 1980, when the recycle rate was just 10% and 90% of MSW was being combusted with energy recovery or disposed of by landfilling.
Typical materials that are recycled include batteries, recycled at a rate of 99%, paper and paperboard at 52%, and yard trimmings at 62%. These materials and others may be recycled through curbside programs, drop-off centers, buy-back programs, and deposit systems.

Recycling prevents the emission of many greenhouse gases and water pollutants, saves energy, supplies valuable raw materials to industry, creates jobs, stimulates the development of greener technologies, conserves resources for our children's future, and reduces the need for new landfills and combustors.
Recycling also helps reduce greenhouse gas emissions that affect global climate. In 1996, recycling of solid waste in the United States prevented the release of 33 million tons of carbon into the air-roughly the amount emitted annually by 25 million cars.
Combustion/Incineration
Burning MSW can generate energy while reducing the amount of waste by up to 90 percent in volume and 75 percent in weight.
Recycling also helps reduce greenhouse gas emissions that affect global climate. In 2006, the national recycling rate of 32.5 percent (82 million tons recycled) prevented the release of approximately 49.7 million metric tons of carbon into the air--roughly the amount emitted annually by 39 million cars, or 1,300 trillion BTUs, saving energy equivalent to 10 billion gallons of gasoline.