In Statistics, the basis of all statistical calculations or interpretation lies in the collection of data. There are numerous methods of data collection. Both are suitable in different cases and the knowledge of these methods is important to understand when to apply which method.
These two methods are the Census method and Sampling method.
Census Sampling
Census method is the method of statistical enumeration where all members of the population are studied. A population refers to the set of all observations under concern. For example, if you want to carry out a survey to find out student’s feedback about the facilities of your school, all the students of your school would form a part of the ‘population’ for your study.
At a more realistic level, a country wants to maintain information and records about all households. It can collect this information by surveying all households in the country using the census method.
In our country, the Government conducts the Census of India every ten years. The Census appropriates information from households regarding their incomes, the earning members, the total number of children, members of the family, etc. This method must take into account all the units. It cannot leave out anyone in collecting data. Once collected, the Census of India reveals demographic information such as birth rates, death rates, total population, population growth rate of our country, etc. The last census was conducted in the year 2011, and due in 2021.
Sampling Method
Like we have studied, the population contains units with some similar characteristics on the basis of which they are grouped together for the study. In the case of the Census of India, for example, the common characteristic was that all units are Indian nationals. But it is not always practical to collect information from all the units of the population.
It is a timeconsuming and costly method. Thus, an easy way out would be to collect information from some representative group from the population and then make observations accordingly. This representative group which contains some units from the whole population is called the sample.
The first most important step in selecting a sample is to determine the population. Once the population is identified, a sample must be selected. A good sample is one which is:
 Small in size.
 It provides adequate information about the whole population.
 It takes less time to collect and is less costly.
In the case of our previous example, you could choose students from your class to be the representative sample out of the population (all students in the school). However, there must be some rationale behind choosing the sample. If you think your class comprises a set of students who will give unbiased opinions/feedback or if you think your class contains students from different backgrounds and their responses would be relevant to your student, you must choose them as your sample. Otherwise, it is ideal to choose another sample which might be more relevant.
Again, realistically, the government wants estimates on the average income of the Indian household. It is difficult and timeconsuming to study all households. The government can simply choose, say, 50 households from each state of the country and calculate the average of that to arrive at an estimate. This estimate is not necessarily the actual figure that would be arrived at if all units of the population underwent study. But, it approximately gives an idea of what the figure might look like.
Different
Census 
Sample Survey 

Definition  A statistical method that studies all the units or members of a population.  A statistical method that studies only a representative group of the population, and not all its members. 
Calculation  Total/Complete  Partial 
Time involved  It is a timeconsuming process.  It is a quicker process. 
Cost involved  It is a costly method.  It is a relatively inexpensive method. 
Accuracy  The results obtained are accurate as each member is surveyed. So, there is a negligible error.  The results are relatively inaccurate due to leaving out of items from the sample. The resulting error is large. 
Reliability  Highly reliable  Low reliability 
Error  Not present  The smaller the sample size, the larger the error. 
Relevance  This method is suited for heterogeneous data.  This method is suited for homogeneous data. 
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