Inferential Statistics




In the last article we discussed about Descriptive Statistics. This blog focuses on Inferential Statistics and tries to differentiate between the two.Inferential Statistics is a type of statistics, that focuses on drawing conclusions about the population, on the basis of sample analysis and observation. Inferential Statistics is all about generalizing from the sample to the population, i.e. the results of analysis of the sample can be deduced to the larger population, from which the sample is taken. It is a convenient way to draw conclusions about the population when it is not possible to query each and every member of the universe. The sample chosen is a representative of the entire population; therefore, it should contain important features of the population. Inferential Statistics is used to determine the probability of properties of the population on the basis of the properties of the sample, by employing probability theory. The major inferential statistics are based on the statistical models such as Analysis of Variance, chi-square test, student’s t distribution, regression analysis, etc. It attempts to reach the conclusion to learn about the population, that extends beyond the data available.  inferential statistics allows us to make predictions (“inferences”) from that data. With inferential statistics, we take data from samples and make generalizations about a population. There are two main areas of inferential statistics:

  1. Estimating parameters. This means taking a statistic from your sample data (for example the sample mean) and using it to say something about a population parameter (i.e. the population mean).
  2. Hypothesis tests. This is where you can use sample data to answer research questions. For example, you might be interested in knowing if a new cancer drug is effective. Or if breakfast helps children perform better in schools.

With this we take that sample data from a small number of people and and try to determine if the data can predict whether the drug will work for everyone (i.e. the population). There are various ways you can do this, from calculating a z-score (z-scores are a way to show where your data would lie in a normal distribution to post-hoc (advanced) testing. In the following articles we will discuss in detail about the methods applied in Descriptive and Inferential Statistics.

 




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