 |
| Population vs sample examples |
This blog" Population vs Sample examples ", also explain about population vs samples.
Population vs Sample examples
Definition of sample and population. A sample is a small portion of the whole population that is used to represent the larger part and that large part from which we select a sample is population.
Example
We have two different cases for our population; either the gender of children of three years in the family and whether it’s boy, girl or any other. As we know that there are 2 girls and 3 boys in our case so we will use “boy” as male case and case by nature for the “girl” but in the real world “female” is not an option. So to make our life easier let’s assume all the cases are only from one sex. Similarly if we consider another scenario which say we have 6 boys and 6 girls then the total number of children would be 12 and it is represented by six boys and four girls. Now, our aim is to find out the percentage of each sex in each case. The question is how many girls and boys are there in 10 kids and so on. Our goal is to calculate the average of the child’s sex in all the cases.
Examples
I have given some examples to understand your concept better. In our first case 1) there are 3 cases with boys. But after considering the case by gender, we consider all the cases the same and we calculate the averages based upon it. For case 1) the gender has not changed; so we will calculate the averages from the data. It is easy to visualize and calculation is very simple. But for case 2) the variable like height and weight is the same. So we calculate those averages and compare them against our initial cases. From the results we can see the result is very close to the average of boys and girls. So finally we have come up with the conclusion that for a case of the age group of 10 years the boys and girls have the same sex ratio. Similarly in case of higher age groups the gender remains constant.
Population vs sample examples
For instance in a big company like Toyota, they have thousands of workers who work under various categories. They have around 200,000 employees. If the number of units sold per year is 5,000 then, the company has around 5000 employees working in that unit. And this amount increases every year. So they do have a huge population.
What do you mean when you say a population?
Population could also be defined as a group of data points with which we can test if certain characteristics exist in relation to the case study. So, we can say that the population consists of all the persons who take part in the study. Also in a study there always exists a random selection of subjects.
This representation of the population represents a sample of the characteristics that were considered while studying. We will go through the examples of a large-scale population such as census, health survey and so on. Then from these examples, we can use a sample to analyze the information.
Difference between sample and population
 |
| Population vs Sample examples |
Sampling refers to finding information about a new group of people. Sampling can be done randomly. As the name suggests it finds a representative sub-group within the population. There are many methods and techniques for selecting data to be sampled as we can give the options of probability sampling or convenience sampling.
Types of sample size
There are several types of sample size including nominal, interval and multi-level sample size, multi-level sampling and systematic sampling. Nominal sample size refers to a single parameter or a group of attributes and it is based on a scale of measurement where the scale is fixed. So for a discrete attribute, the size of the subgroup is equal to its maximum value among all values of the attribute. To illustrate, suppose you have a square data with a square grid and then divide it into smaller squares, such as size of a rectangular. Suppose you choose to have 4 squares, in our case 4 sq. If the scale is X, then we select 4 squares of size = X. We call this sample size. On the other hand, interval sampling refers to choosing a representative interval or a subgroup from the entire population. Interval sample size is usually small and it can be used at least once before drawing the conclusions.
Multi-level sample size refers to a group of variables, where, the sample size varies according to the size of the subgroup. So, for example, for an array of numbers, we have several parameters such as count, count rate, etc, for count rate for each square. So to determine the sample size for n-levels of the parameter, the size of the square becomes 1. So a few samples of the actual scale are selected. Each level is called an interval. Thus the sample size for the set of n intervals will be ‘n=n-1’. You may take a few samples in the interval size ‘n’ to get the sample size ‘n-1’.
Systematic sampling refers to using a systematic method for choosing a representative subgroup from the entire population. It is similar to multi-level sampling in the sense that it chooses a smaller group within the population as a representative sample for the entire population. However, the difference is that in the case of systematic sampling, we have several samples before getting the final sample for the whole population. So it uses a larger sample size. However the difference is that the size of the sample is much more than the interval. So the size of the population becomes very small compared to the interval size ‘n’.
Conclusion
On the basis of this sample size and population size, one can say that it could not be considered as a representative subgroup as well.
0 Comments