What Are Simple Random Sampling and Stratified Random Sampling Analytical Techniques?
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This document discusses sampling techniques used in data analysis. It defines sampling as selecting a subset of data to represent a larger population. Two common sampling methods are described: Simple random sampling involves randomly selecting items from the entire population so that each item has an equal chance of selection. Stratified random sampling first divides the population into subgroups, then randomly samples from each subgroup in proportion to its size. This ensures subgroups are represented in the sample. Advantages and disadvantages of each method are provided. Simple random sampling is economical but risks not being representative. Stratified random sampling has less chance of bias and higher accuracy but requires defining subgroups first. Sampling allows analyzing large populations efficiently while capturing overall characteristics.
What Are Simple Random Sampling and Stratified Random Sampling Analytical Techniques?
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- 2. SAMPLING
- 3. What is Sampling A technique of selecting a representative part of a population for the purpose of determining characteristics of the whole population to save cost , time and efforts Thus, sample data is the subset of population data used to represent the entire group as a whole
- 4. Popular sampling methods Simple Random Sampling: Here, the selection is purely based on a chance and every item has an equal chance of getting selected Lottery system is an example of simple random sampling Stratified Random Sampling: Here, the population data is divided into subgroups known as strata The members in each of the subgroup formed have similar attributes and characteristics in terms of demographics, income, location etc. A random sample from each of these subgroups is taken in proportion to the subgroup size relative to the population size These subsets of subgroups are then added to from a final stratified random sample Higher statistical precision is achieved through this method due to low variability within each subgroup, also less sample size is required for this method of sampling when compared to simple random sampling
- 5. Example : Simple Random SamplingForinstance if we want to come up with average value of all cars in united states, it is impractical to assess the each car value in united states, adding these numbers and dividing by total number of cars Instead we can randomly select some of the cars, say 200 and get value of each of these 200 cars and find average of these 200 numbers These 200 numbers containing randomly selected 200 cars’ values is called a sample data of entire United states’ cars’ values (population data)
- 6. Example : Simple Random SamplingTakinganotherexample The total workforce in organizations is 300 and to conduct a survey, a sample group of 30 employees is selected to do the survey Inthiscase The population is the total number of employees in the company and the sample group of 30 employees is the sample Eachmember Of the workforce has an equal opportunity of being chosen because all the employees which were chosen to be part of the survey were selected randomly. But, There is always a possibility that the sample does not represent the population as a whole
- 7. Example : Stratified Random Sampling It is used when the researcher wants to examine subgroups within a population For example, one might divide a sample of adults into subgroups by age, like 18-29, 30-39, 40-49, 50-59, and 60 and above To stratify this sample, the researcher would then randomly select proportional amounts of people from each age group This is an effective sampling technique for studying how a trend or issue might differ across subgroups Some of the most common strata used in stratified random sampling include age, gender, religion, race, educational attainment, socioeconomic status, and nationality Thus, with stratified sampling, the researcher is guaranteed that the subjects from each subgroup are included in the final sample, whereas simple random sampling does not ensure that subgroups are represented equally or proportionately within the sample
- 8. Pros and Cons : Simple Random Sampling Economical in nature Less time consuming Chances of bias Difficulty of getting the representative sample
- 9. Pros and Cons : Stratified Random Sampling Economical in nature Less time consuming Very less chances of bias compared to Simple random sampling Higher accuracy than Simple random sampling Need to define the categorical variable by which sub groups should be created : For instance Age group , Gender, Occupation , Income group, Education, Religion, Region etc.
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