2.6. CENSUS AND SAMPLE SURVEYS

According to the Census 2011, population of India was 121.09 crore, which was 102.87 crore in 2001. Census 1901 indicated that the population of the country was 23.83 crore. Since then, in a period of 110 years, the population of the country has increased by more than 97 crore. The average annual growth rate of population which was 2.2 per cent per year in the decade 1971-81 came down to 1.97 per cent in 1991-2001 and 1.64 per cent during 2001-2011.

A census is a complete survey that includes every member of the population. In India, it is carried out every ten years, collecting comprehensive data about the entire population. This includes demographic information like birth and death rates, literacy levels, and other key statistics that help in understanding the dynamics of the country's population growth over decades. The 2011 Census reported a population of 121.09 crore, showing significant growth from 102.87 crore in 2001, which means the country added approximately 18 crore people in just ten years.

Imagine tallying the number of students in a school at the end of the school year. Just as every student is counted to know how many attended and graduated, a census counts every individual in a country to get a true understanding of the population size and growth.

Population or the Universe in statistics means totality of the items under study. Thus, the Population or the Universe is the group to which the results of the study are intended to apply. A population is always all the individuals/items who possess certain characteristics (or a set of characteristics), according to the purpose of the survey. The first task in selecting a sample is to identify the population. Once the population is identified, the researcher selects a method of studying it.

In statistics, a population is the entire group being studied, while a sample is a smaller section of that population that is selected for analysis. Identifying the population is essential to ensure that the results of the sample can be accurately generalized to the entire group. For example, if a researcher wants to investigate farmers' incomes, the population would consist of all farmers, and the sample might be a selected group of farmers from various locations to represent the whole.

Think of a large jar filled with various jellybeans. The entire jar represents the population, while if you take a handful of jellybeans to taste, that handful is your sample. You hope that the taste of the handful accurately represents the taste of all the jellybeans in the jar.

If the researcher finds that a survey of the whole population is not possible, then he/she may decide to select a Representative Sample. A sample refers to a group or section of the population from which information is to be obtained. A good sample (representative sample) is generally smaller than the population and is capable of providing reasonably accurate information about the population at a much lower cost and shorter time.

A representative sample is crucial because it reflects the characteristics of the larger population. This means researchers can gather insights and make conclusions about the whole group without having to survey everyone, which saves time and resources. The accuracy of the sample must be high enough so that findings can be trusted and applied to the broader population. Examples include surveying a portion of voters during elections or testing a few products instead of every item produced.

If you want to understand the taste preference of all students in a school for a special event food, you wouldn’t ask every student. Instead, you might survey a small group of students (your sample) that represents different classes, grades, or cuisines. Their feedback would help you decide what food to serve to everyone.

There are two main types of sampling, random and non-random. In random sampling, every individual has an equal chance of being selected. In non-random sampling, the selection is based on judgment or convenience rather than random choice.

Random sampling ensures that every member of the population has an equal opportunity to be chosen, which minimizes bias and enhances the representativeness of the sample. This method can involve using random number generators or drawing names from a hat. Non-random sampling, on the other hand, may involve selecting individuals based on specific traits or the convenience of access, which can lead to biased results since certain members of the population might be left out or overrepresented.

Consider a box of assorted cookies. If you want to taste randomly, you might blindfold yourself and pick cookies without looking (random sampling). But if you only take cookies from the top of the box because they're easiest to grab, you might miss the rare flavors hidden underneath (non-random sampling).

You must have seen that when an election takes place, the television networks provide election coverage. They also try to predict the results. This is done through exit polls, wherein a random sample of voters who exit the polling booths are asked whom they voted for.

Exit polls are a practical example of random sampling in action. By asking a sample of voters who just cast their ballots, television networks can gather data that helps predict election outcomes. The information from these voters is then extrapolated to give a snapshot of how the entire voting population may have voted, illustrating the power of sampling methods in understanding larger trends.

Imagine after a big game, reporters ask a few fans exiting the stadium who they think won and by how much. Those opinions can help gauge the overall sentiment among all fans, even though only a small group is surveyed. This is akin to exit polls predicting election results based on the views of a few voters.

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