Importance of Population and Sampling
History from Political Arithmetic to Statistics
The history timeline show evidence of statistical data as early as Ancient Greece time but records show statistics in late 16th century, when it was introduced by, John Graunt, William Petty, and Pascal and later in 17th century by Gottfried Achenwall. It was an exciting time when success and discoveries raised the confidence of scientists, physicist and astronomers to think that laws of nature are not of divine intervention. As the time evolved and new discoveries were attained from political arithmetic like, mortality demographics, census data, economy, and International Statistical Congresses, they all led to changing its name to ‘statistics’. Population

Every 10 years the country conducts a census of population to provide data that can be of use for research, business marketing, planning, surveys, and different sampling. The first U.S. census took place in 1790. What is ‘population’? The common term “population” describes people that live in a town which is located in a certain region within a certain county or state and their respective characteristic such age, sex, ethnicity, marital status, or other. The statistic term “population” consist of all members, elements of the defined group. It includes all subjects to be studied or collecting information on for data driven decisions. The basic population characteristics are birth, growth, aging, and death Sample

In the effort of obtaining statistic data from population different factors are collected, analyzed, and summarized to come to a conclusion. To collect a certain data from the population a sample is performed. A part of the population is selected sometimes randomly with the same characteristics. The characteristics sample techniques are performed to save time, are suitable for different types of data or surveys, and saves money. The essential...

...SamplingSampling methods are classified as either probability or nonprobability.
In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling.
In nonprobability sampling, members are selected from the population in some non random manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling.
The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In nonprobability sampling, the degree to which the sample differs from the population remains unknown.Stratified sampling techniques are generally used when the population is heterogeneous, or dissimilar, where certain homogeneous, or similar, sub-populations can be isolated (strata). Simple random sampling is most appropriate when the entire population from which the sample is taken is homogeneous. Some reasons for...

...SAMPLING DISTRIBUTIONS
|6.1 POPULATION AND SAMPLING DISTRIBUTION |
|6.1.1 Population Distribution |
Suppose there are only five students in an advanced statistics class and the midterm scores of these five students are:
70 78 80 80 95
Let x denote the score of a student.
• Mean for Population
Based on Example 1, to calculate mean for population:
[pic]
• Standard Deviation for Population
Based on example 1, to calculate standard deviation for population:
[pic]
|6.1.2 Sampling Distribution |
▪ Sample statistic such as median, mode, mean and standard deviation
6.1.2.1 The Sampling Distribution of the Sample Mean
Reconsider the population of midterm scores of five students given in example 1. Let say we draw all possible samples of three numbers each and compute the mean.
Total number of samples = 5C3 =[pic]...

...Sampling Methodologies
Population:
Population is defined as including all items with the characteristic one wishes to understand. Because there is seldom enough time or money to gather information from everyone or everything in a population, the goal is to find a representative sample (or subset) of that population.
For example, a researcher might study the success rate of a new 'quit smoking' program on a sample group of 50 patients, in order to predict the effects of the program if it were made available nationwide. Here the superpopulation is "everybody in the country, given access to this treatment" - a group which does not exist, since the program isn't yet available to all.
Sampling Frame:
Sampling Frame refers to the selection of a subset of individuals/items from a population to form the sample for our survey. There are two types of sampling methods: Probability Sampling and Non-Probability Sampling.
Definition and Difference between Probability Sampling and Non-Probability Sampling
Probability methods require a sample frame.Probability methods rely on random selection in a variety of ways from the sample frame of the population. They permit the use of higher level statistical techniques and allow you to calculate the difference between your sample results and...

...Evaluation
Professor: Dr. Elidio T. Acibar
Reporter: Evelyn L. Embate
Topic: SamplingSAMPLING
Measuring a small portion of something and then making a general statement about the whole thing.
Advantages of samplingSampling makes possible the study of a large, heterogeneous population
It is almost impossible to reach the whole population to be studied. Thus, sampling makes possible this kind of study because in sampling only a small portion of the population may be involved in the study, enabling the researcher to reach all through this small portion of the population.
* Sampling is for economy
A research without sampling may be too costly. For an instance if you are to take the whole population, it will take you an expensive cost because of the number of questionnaire copies.
* Sampling is for speed
A research without sampling might be too time consuming. If a research takes a long time to finish, there may be many intervening factors that deter the researcher from finishing his research.
* Sampling is for accuracy
A time too long to cover the whole study population, may ne inaccurate. By the time the last person is interviewed, the data gathered from the first interviewees may be obsolete already so that the...

...Copyright 2010 Graham Elliott. All Rights Reserved.
Sampling
We are now putting all of the pieces together. Considering each observation xi as an outcome from a random variable Xi , we have that functions g(x1 ; x2 ; :::; xn ) are draws from the random variable
Pn g(X1 ; X2 ; :::; Xn ):
For 120a the function we are interested in is the sample mean — g(x1 ; x2 ; :::; xn ) = n1 i=1 xi : In this chapter
we work with this function for distributions with many random variables.
1
From the Text
Question 1. Problem 6-10, p.206
Question 2: Problems 6-16,6-22.
Question 3: Problem 6-36.
2
Sampling Questions
Question 1. A study suggests that in a trial of 50 people the average decrease in cholestorol from using a
particular drug is 18 points with a standard deviation of 10 points.
(a) What is the chance that, given the true e¤ect is a reduction of 20 points, that a particular person
has no decrease in cholestorol when they take this drug?
(b) A reporter writes a newspaper article discussing the drug. They report that although the study claims
that the reduction is 18 points, they have found a user that actually had no change to their cholestorol. They
use this persons story to argue that the results are misleading. What is the chance that, if the real e¤ect
was zero, that the average in a trial might be 18 just by chance?
(c) Is the counter example found by the reporter useful in understanding the results for the study?
Question 2. In the questions...

...
Life Sciences Sampling and Populations Paper
MTH/231
Life Sciences Sampling and Populations Paper
The core of biostatistics consists of the definition of a population and sampling, as they are the indicators of the fundamental concepts that are essential to understanding the statistics of the life and health sciences. The idea that a sample is illustrative of a given population, since a sample is derived from a specific, yet larger pool of information seems factually representative. Random sampling aides research in that it applies experimental design to the selection process and is the fairest means of sample collection, providing equal chance to the members of a given population being signified.
PopulationsPopulations, as defined by Triola and Triola (2006) are “a complete collection of all elements (scores, people, measurements and so on) to be studied. The collection is complete in the sense that it includes all the subjects to be studied. To serve as an example, all Kansan 9th graders will functionally be considered for a population. The data collected from the population are referred to as parameters and therefore are descriptive. The subjects or observations within the population are labeled with an N, or in the case of the example, figuratively, would be...

...Purposive sampling
Purposive sampling, also known as judgmental, selective or subjective sampling, is a type of non-probability sampling technique. Non-probability sampling focuses on sampling techniques where the units that are investigated are based on the judgement of the researcher.
Purposive sampling explained
Purposive sampling represents a group of different non-probability sampling techniques. Also known as judgmental, selectiveor subjective sampling, purposive sampling relies on the judgement of the researcher when it comes to selecting the units(e.g., people, cases/organisations, events, pieces of data) that are to be studied. Usually, the sample being investigated is quite small, especially when compared with probability sampling techniques.
Unlike the various sampling techniques that can be used under probability sampling (e.g., simple random sampling, stratified random sampling, etc.), the goal of purposive sampling is not to randomly select units from a population to create a samplewith the intention of making generalisations (i.e., statistical inferences) from that sample to the population of interest. This is the general intent of research that is guided by a quantitative research...

...“Deciding on a sampling procedure for a study on understanding teaching and learning relations for minority children in Botswana classrooms”
Sampling is a very important statistical tool used by researchers to find accurate results that represents the complete attributes of population.
Different types of sampling are used for different type of data. For example: probability sampling is used for quantitative data as attributes of such data can easily be generalized to population.
While dealing with the qualitative data, on probabilistic methods of sampling are used because such data represents social attributes. Researchers use generalization, comparability, totalization or transitivity methods for qualitative data.
To understand the learning and teaching relations of minority children in Botswana school, we have to understand the social, cultural and learning environment of such kids. Then the role of teacher and their teaching methods are analyzed.
Researchers like Le Compte, Preissle and Merriam say that population or data units are basics to determine the sampling procedures that are used for sampling.
As per the nature of the analyzing learning and teaching relations of Botswana children, we use purposive sampling strategy to analyze difference between school...