Introduction to Statistics:Unlocking the Power of Data
Are you an aspiring Data Scientist and want to know how the magic happens with data in this trend? Its simple answer is Statistics, there is no any magic happening in algorithms, it’s just maths of stats which is doing this magic. The building block of Data Science is Statistics. Let’s understand the basics of statistics.
In today’s world, the almost everything is based on Data-Driven decision-making. Let’s understand this thing by taking a simple example of well known company, OLA. It gives service named LUX premium where a customer can book a cab which include premium vehicles like AUDI, BMW even JAGUAR. But it is not available in every city as majority people of every city are not that much richer who can afford this price. Then how OLA know in which city they have to launch this service. It answers by seeing the real data of cities on basis of which they decide, and that whole thing is known as Data-Driven decision-making. When there is data, then we can apply our data science algorithms whose building block is stats.
Now, after knowing the importance of statistics, let’s understand its basic.
What is Statistics
Statistics is the discipline that concerns about the collection, organizing, analysis, interpretation, and presentation of data.it’s the tool that enables us to make sense of the chaos of information and draw meaningful conclusions. Whether it’s predicting the outcome of elections, understanding the effectiveness of a new drug, or analysing consumer preferences, statistics plays a vital role in nearly every aspect of our lives.
Types of Statistics
Descriptive Statistics
These methods are all about summarizing and presenting data in a clear and meaningful way. Common techniques include measures of central tendency (like the mean, median, and mode) and measures of dispersion (such as variance and standard deviation). Descriptive statistics help us make sense of raw data by providing a snapshot of its key characteristics.
Let’s understand by real example what really descriptive statistics is,
Imagine you have a group of friends, each with a different age. Descriptive statistics would help you find the average age (mean) of your friends, the age of the friend right in the middle (median), and maybe even the age that occurs most frequently (mode). It also gives you a sense of how much the ages vary from the average (standard deviation), and whether the ages are evenly spread or clustered at specific points. In essence, descriptive statistics provides a concise summary that reveals the essential characteristics and patterns within your data, making it easier to grasp and interpret.
Inferential Statistics
This branch of statistics takes things a step further. It involves making predictions, inferences, or generalizations about a population based on a sample of data. Techniques like hypothesis testing and confidence intervals fall under this category. Inferential statistics allows us to draw conclusions about a larger group by examining a smaller, representative sample.
Let’s understand by real example what really Inferential statistics is,
Imagine on Election Day, a news organization conducts exit polls by surveying a random sample of voters as they leave the polling stations, asking them about their candidate preferences. From this sample, they find that 60% of respondents voted for Candidate A and 40% for Candidate B. Inferential statistics takes this sample data and enables us to make an educated guess about the broader population of voters who participated in the election. This suggests that Candidate A may have a substantial lead. It’s akin to predicting the outcome of the entire election based on the insights gathered from a representative subset of voters. Inferential statistics in exit polls plays a pivotal role in offering early insights into election results, even before all votes are counted, shaping public perception and news coverage.
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