User:Michel Jonathan Levinson/sandbox

 International Motor Vehicle Sociology Study 

A 2018 study conducted by HMRF (Hemsworth Motor Research Foundation) found that 85% of people who went on motorbike trips with friends or cousins in their youth without the supervision of elders developed good social skills; while 15% developed excellent social skills. This direct relationship between social skills and motorbike trips accounts for their ending up with high-paying jobs, that require the application of excellent social skills.

Professor Jeff Hardy (Head of Psychoanalysis Department) at HMRF says, "Such trips provide exposure at the right time, when the individual is beginning to develop a professional and social profile. An increased sense of responsibility later reflects in other aspects of life, and the eventual result is a better and more responsible life."

The same study also found that 60% of people who did not undertake such trips in their youth struggled in terms of social skills and had comparatively low paying jobs. The rest 40%, however, had equally good social skills and equally high paying jobs as those who undertook such trips, but these individuals did go for outing of some kind in their youth without the supervision of adults.

Following the publication of the study, several universities around the globe initiated research projects in what has now come to be called as "motor-sociology". Presstown University has been offering undergraduate and postgraduate degree and diploma courses in motor-sociology since 2017.

Mr. Elon Zuckerberg (Head of Marketing Department) at HMRF supervised the sample investigation of International Two-Wheeler survey data collection

Population Parameter and Sample Statistic
The reason for conducting a sample survey is to estimate the value of some attribute of a population.


 * Population parameter. A population parameter is the true value of a population attribute.
 * Sample statistic. A sample statistic is an estimate, based on sample data, of a population parameter.

Consider this example. A public opinion pollster wants to know the percentage of voters that favor a flat-rate income tax. The        actual percentage of all the voters is a population parameter.The estimate of that percentage, based on sample data, is a sample statistic.

The quality of a sample statistic (i.e., accuracy, precision, representativeness) is strongly affected by the way that sample observations are chosen; that is., by the sampling method.

Probability and Non-Probability Samples
As a group, sampling methods fall into one of two categories.


 * Probability samples. With probability sampling methods, each population element has a known(non-zero) chance of being chosen for the sample.
 * Non-probability samples.With non-probability sampling methods, we do not know the probability that each population element will be chosen, and/or we cannot be sure that each population element has a non-zero chance of being chosen.

Non-probability sampling methods offer two potential advantages - convenience and cost. The main disadvantage is that non-probability sampling methods do not allow you to estimate the extent to which sample  statistics are likely to differ from population parameters. Only probability sampling methods permit that kind of analysis.

Non-Probability Sampling Methods
Two of the main types of non-probability sampling methods are voluntary samples and convenience samples.


 * Voluntary sample. A voluntary sample is made up of people who self-select into the survey. Often,  these folks have a strong interest in the     main topic of the survey. Suppose, for example, that a news show asks viewers to    participate in an on-line poll.  This would be a volunteer  sample.  The sample is chosen by the viewers, not by the survey administrator.
 * Convenience sample. A convenience sample is made up of people who are easy to reach. Consider the following example. A pollster interviews shoppers  at a local mall.  If the  mall was chosen because it was a convenient site from which   to solicit survey participants and/or because it was close to the  pollster's home or business, this would be a convenience  sample.

Probability Sampling Methods
The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage        sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the  population. This ensures that the statistical conclusions will be valid.


 * Simple random sampling. Simple random sampling refers to any sampling method that has the following properties.
 * The population consists of N objects.
 * The sample consists of n objects.
 * If all possible samples of n objects are equally likely to occur, the sampling method is called simple random            sampling.  There are many ways to obtain a simple random sample.  One way would be the lottery method. Each of the N population members         is assigned a unique number. The numbers are placed in a bowl and thoroughly mixed. Then, a blind-folded researcher selects n numbers.         Population members having the selected numbers are included  in the sample.
 * Stratified sampling. With stratified sampling,        the population is divided into groups, based on some characteristic.        Then, within each group, a probability sample (often a simple random  sample) is selected.  In stratified sampling, the groups are          called strata. As a example, suppose we conduct a national survey.  We might divide the population into groups or strata, based on geography - north,  east, south, and west.  Then, within each stratum, we might randomly select survey respondents.
 * Cluster sampling. With cluster sampling, every  member of the population is assigned to one, and only one, group.   Each group is called a cluster.  A sample of clusters   is chosen, using a probability method (often simple random sampling).   Only individuals within sampled clusters are surveyed. Note the difference between cluster sampling and stratified sampling.  With stratified sampling, the sample includes elements from each  stratum.  With cluster sampling, in contrast, the sample includes elements only from sampled clusters.
 * Multistage sampling. With multistage sampling,  we select a sample by using combinations of different sampling methods. For example, in Stage 1, we might use cluster sampling to   choose clusters from a population.  Then, in Stage 2, we might use  simple random sampling to select a subset of elements from each    chosen cluster for the final sample.
 * Systematic random sampling. With systematic  random sampling, we create a list of every member of the population.  From the list, we randomly select the first sample element from the   first k elements on the population list. Thereafter, we select every kth element on the list. This method is different from simple random sampling since every possible sample of n elements is not equally likely.