Understanding Statistics Types: Simplified for Beginners

Introduction:

Statistics, a subject we encounter from our school days, often intimidates many. Yet, its importance spans across various industries, highlighting its significance. In this blog post, I aim to simplify this subject, making it easier to grasp. Let's dive in together.

Types:

Let's start by discussing the two main types: Descriptive and Inferential.

Well! what are they?

When you say "descriptive," you would think of it as 'detailed,' right? Yeah, that's the meaning. In statistics, it refers to considering the entire population. When you say "inferential", you would think of it as 'getting something from something', right? In statistics, it refers to considering a small sample from the entire population.

Now, let's delve into Descriptive Statistics! Stay tuned for our next blog post, where we'll explore Inferential Statistics.

 

Fig. Descriptive Statistics

Central tendency is a way of figuring out where the middle of a bunch of numbers is. It helps us understand the general trend of our data without having to look at every single value. 

One way we do this is by finding the average, which is also called the Mean. 

Fig. Mean is a Balance point

Variability in statistics refers to the extent to which data points in a dataset differ from each other. It's a measure of dispersion or spread within the data. Variability helps us understand the diversity or range of values present in the dataset.

Standard deviation, a key measure of variability, quantifies how spread out the data are from the mean.

Fig. In the first plot, the standard deviation is greater compared to the second plot because numerous data points are situated at each extreme end. Conversely, in the second plot, a considerable number of data points are clustered around the mean.

Measures of distribution analyze how data is spread out in a dataset. Skewness, a crucial measure, indicates the asymmetry of the data distribution. Positive skewness means data is concentrated on the left with outliers on the right, while negative skewness is the opposite. Understanding skewness helps in recognizing patterns and anomalies within the data. 

Fig. Impact of outliers on mean and median

In a left-skewed distribution, the mean is shifted to the left of the median.

Conclusion:

In conclusion, we've covered the basics of statistics, focusing on descriptive statistics.  By understanding descriptive statistics, we can effectively analyze and interpret data to gain meaningful insights.