Sampling Methods used in Statistical Analysis
From the food you eat to the television you watch, from
political elections to school board actions, much of your life is regulated
by the results of sample surveys.
A sample is a group of units selected from a larger group (the population).
By studying the sample, one hopes to draw valid conclusions about the
larger group. A sample is generally selected for study because the population
is too large to study in its entirety. The sample should be representative
of the general population. This is often best achieved by random sampling.
Also, before collecting the sample, it is important that one carefully
and completely defines the population, including a description of the
members to be included. A common problem in business statistical decision-making
arises when we need information about a collection called a population
but find that the cost of obtaining the information is prohibitive. For
instance, suppose we need to know the average shelf life of current inventory.
If the inventory is large, the cost of checking records for each item
might be high enough to cancel the benefit of having the information.
On the other hand, a hunch about the average shelf life might not be
good enough for decision-making purposes. This means we must arrive at
a compromise that involves selecting a small number of items and calculating
an average shelf life as an estimate of the average shelf life of all
items in inventory. This is a compromise, since the measurements for
a sample from the inventory will produce only an estimate of the value
we want, but at substantial savings. What we would like to know is how"good" the
estimate is and how much more will it cost to make it"better". Information
of this type is intimately related to sampling techniques. This section
provides a short discussion on the common methods of business statistical
sampling.
Cluster sampling can be used whenever the population is homogeneous
but can be partitioned. In many applications the partitioning is a result
of physical distance. For instance, in the insurance industry, there
are small"clusters" of employees in field offices scattered about the
country. In such a case, a random sampling of employee work habits might
not required travel to many of the"clusters" or field offices in order
to get the data. Totally sampling each one of a small number of clusters
chosen at random can eliminate much of the cost associated with the data
requirements of management.
Stratified sampling can be used whenever the population can be
partitioned into smaller sub-populations, each of which is homogeneous
according to the particular characteristic of interest.
Random sampling is probably the most popular sampling method used in
decision making today. Many decisions are made, for instance, by choosing a
number out of a hat or a numbered bead from a barrel, and both of these methods
are attempts to achieve a random choice from a set of items. But true random
sampling must be achieved with the aid of a computer or a random number table
whose values are generated by computer random number generators.
Cross-Sectional Sampling:Cross-Sectional study the observation of a
defined population at a single point in time or time interval. Exposure and
outcome are determined simultaneously.
What is a statistical instrument? A statistical instrument is any
process that aim at describing a phenomena by using any instrument or device,
however the results may be used as a control tool. Examples of statistical
instruments are questionnaire and surveys sampling.
What is grab sampling technique? The grab sampling technique is to
take a relatively small sample over a very short period of time, the result
obtained are usually instantaneous. However, the Passive Sampling is
a technique where a sampling device is used for an extended time under similar
conditions. Depending on the desirable statistical investigation, the passive
sampling may be a useful alternative or even more appropriate than grab sampling.
However, a passive sampling technique needs to be developed and tested in the
field.
Back to Statistical
Forecasting Home Page | 
