What are some statistical tests that we are most familiar with?

Z-test, Studentâ€™s T-test, Paired T-test, ANOVA, MANOVA? Actually they all belong to the Parametric statistics family which assumes that sample data come from a population that can be adequately modeled by a probability distribution that has a fixed set of parameters (mean, standard deviation) aka Normal Distribution.

**Parametric tests usually assume three things:**

- Independence of cases: samples are independent observations
- Normality: sample data come from a normal distribution (or at least is symmetric)
- Homogeneity of variances: sample data come from a population with the same variance

However, in real life, these assumptions can hardly be met. Non-Parametric Tests have much more relaxed assumptions and they are either distribution-free or having a specified distribution but with the distributionâ€™s parameters unspecified.

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Hypothesis testing is common in statistics as a method of making decisions using data. For that confession of data, Hypothesis Testing could be used to interpret and draw conclusions about the population using sample data. A Hypothesis Test helps in making a decision as to which mutually exclusive statement about the population is best supported by sample data.

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In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines. In this post, we'll learn Data Science vs Big Data: Difference Between Data Science & Big Data.