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Home / Blog / Interview Questions on Data Science / Top Inferential Statistics Interview Questions & Answers for 2025
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Making statements or concluding/generalizing for entire population would not be possible, as we will not get the complete data. (Example: who will win the elections).
In order to make these statements, we will work on the subset of the data (sample data).
The results obtained on the sample are called Sample Statistics, and these statistics help us to infer the population. The process of Inferencing statements for population based on the statistics obtained from sample data is called as Inferential Statistics. (Refer to the book for more information here)
The test statistic guides us on estimating the likelihood of a condition about the population (population parameter).
The test measures the chance (probability) of the condition being true.
The measure of significance is the Probability of our estimation about the population is true.
With the test, we reach a degree of confidence, represented as a p-value of the test. p-value will tell us about how likely we have made the right inferences about the population based on the sample statistics.
Sampling is a process of collecting/gathering a subset of data from the population. Sampling can be done in 2 broad ways - Probability & Non-Probability techniques.
Probability Technique: Also known as the Unbiased method, where equal opportunity is given to all the values of the population.
Non-Probability Technique: Also known as the Biased method, where unequal opportunity is given to the data points of the population.
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The sampling technique which allows each and every data value from the population to be selected into the sample with equal opportunity is known as Probability sampling or Unbiased sampling.
Examples of Unbiased Sampling are:
The sampling technique which gives varied chances to the data values of the population to get selected into the sample is known as Non-probability sampling or Biased sampling.
Examples of Biased Sampling are:
A population parameter is a constant that is not generally known and needed to be estimated from a statistic, a function of sample values. The sample values are subjected to change, as it depends upon sample values which are chosen at random, hence statistics are not constant.
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