Inferential vs Descriptive Statistics
Inferential statistics and descriptive statistics are two branches of statistics that serve different purposes:
- Descriptive Statistics: Descriptive statistics is concerned with describing and summarizing the features of a dataset. It involves methods such as calculating measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation), and visualizing data through graphs and charts (histograms, box plots). Descriptive statistics are used to understand the basic characteristics of the data, such as its distribution, variability, and central tendency.
- Inferential Statistics: Inferential statistics, on the other hand, involves using sample data to make inferences or draw conclusions about a larger population. It allows researchers to generalize their findings from the sample to the population and to make predictions or hypotheses about the population based on the sample data. Inferential statistics includes techniques such as hypothesis testing, confidence intervals, and regression analysis. These techniques help researchers assess the reliability of their findings and determine whether they are likely to apply to the broader population.
What is Inferential Statistics?
In the world of data analysis, statistics plays a big role in helping us understand patterns and insights from raw data. Descriptive statistics help us summarize and describe data, while inferential statistics take us a step further by letting us make predictions and decisions about a larger group based on a smaller sample.
In this article, we’ll dive into inferential statistics, looking at why it’s important, how it works, and where it’s used.