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Must know Statistical Methods to succeed in the field of Data Science

- 8 minutes read | By Harshit Singh

Statistics Introduction

Statistics is a powerful tool used across various fields like science and business to make informed decisions. It provides us with accurate knowledge that helps us understand and predict outcomes. In this blog, we'll delve into the world of statistics, its typical steps, and its various applications.

Typical Steps of Statistical Methods

When dealing with statistics, there are some typical steps to follow:

How is Statistics Used?

Statistics helps us understand populations by studying samples. A population is the entire group you want to learn about, while a sample is a smaller part of that population. Statistical methods are applied to samples, and the results are used to make conclusions about the entire population.

Descriptive Statistics

Descriptive statistics is the process of summarizing and presenting data in a meaningful way. It helps us understand the center, variation, and shape of data.

Statistical Inference

Statistical inference involves using probability theory to estimate population parameters based on sample statistics. Confidence intervals and hypothesis testing are important tools in statistical inference. Confidence intervals indicate the range in which a parameter is likely to fall, while hypothesis testing checks if a statement about a population is true based on sample data.

Causal Inference

Causal inference is used to determine if one thing causes another. For example, it helps answer questions like, "Does rain make plants grow?" However, establishing causality often requires careful experimental design, which can be challenging.


Statistics allows us to make predictions about future events, known as forecasts. These predictions are based on patterns and trends observed in the data.


When making conclusions about causality, it's crucial to do so carefully and consider alternative explanations for observed phenomena.

Population and Samples

Different Types of Sampling Methods

Different Types of Data

Measurement Levels

Statistics - Descriptive Statistics

Descriptive statistics focus on key features of data:

Frequency Tables

Frequency tables organize data into a table, helping to count and order data into intervals.

Visualizing Data

Different types of graphs and charts are used to represent data effectively, including pie charts, histograms, scatter plots, and box plots.

Average (Measures of Central Tendency)

The center of data is described by various measures of central tendency, including:

Statistics - Inferential Statistics

Inferential statistics use sample data to make inferences about the population:

Normal Distribution

The normal distribution is a bell-shaped probability distribution described by mean (μ) and standard deviation (σ). It's characterized by features like symmetry, a single mode, and defined probabilities for different intervals.

Z-Values and Standard Normal Distribution

Z-values express how many standard deviations a value is from the mean. The standard normal distribution (Z-distribution) has a mean of 0 and a standard deviation of 1, allowing for standardization of data.

Chi-Square Distribution

The chi-squared test is used to compare observed and expected data and assess if the difference is due to chance or a relationship between variables. It is commonly used for categorical data.

ANOVA (Analysis of Variance)

ANOVA is used to analyze variance in data and assess the impact of one or more independent variables on a dependent variable. It comes in two main types: one-way and two-way ANOVA.

Power Law Distribution/Pareto Distribution

Power law distributions, also known as Pareto distributions, describe situations where smaller numbers are more common than larger ones. This distribution is often seen in phenomena where a few entities dominate the majority.

In Conclusion

In conclusion, statistics is a versatile tool that empowers decision-making by providing insights into data, enabling predictions, and facilitating the exploration of relationships between variables. Understanding the fundamentals of statistics and its various methods is essential for making informed choices in a data-driven world.

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Work done byHarshanz for iamdata