A data set is a systematic collection of facts and statistics that can be used for a particular purpose, such as a survey. In data analytics, statistics serve as the brains. They identify patterns and trends that can lead to a better understanding of problems. In essence, they give information life and aid in its interpretation.
In terms of data, there are two types: qualitative and quantitative. Qualitative data is defined as qualitative and depicting qualitative descriptions that can be observed and described. But qualitative data cannot be subjected to any mathematical calculation.
On the other hand, quantitative data can be quantified, observed, represented numerically, and computed.
The present-day researcher is flooded with a torrent of qualitative and quantitative data generated by representative research or simple monitoring and digitization procedures.
Let’s try to understand the key concept of population and sample before we move on to understanding the research process.
What do statistics mean by population and sample?
The concepts of population and sample are two of the most basic but crucial concepts in statistics. Their definition can be summed up as follows.
- Population refers to a broader group of people undergoing research, whereas a sample refers to a subset of that larger group.
- Sampling: As the first step in any research process, sampling involves selecting a representative sample from the entire population to conduct a statistical analysis. Making inferences about the population involves describing the sample and extrapolating the findings to the population. Together, these steps encapsulate the essence of both descriptive and inferential statistics.
Statistics is the science that collects, organizes, and evaluates data. Research today relies heavily on statistics, which can be used to quantify, analyze, and present data. Since most people are unfamiliar with descriptive or inferential statistics, deciding which type to use can be challenging.
With the concepts of population and sample under our belts, it’s time to explore descriptive and inferential statistics.
A descriptive statistic is one that describes the essential qualities or properties of data using parameters such as mean, median, mode, range, standard deviation, and variance. The data can be accurately summarized and shown using charts, tables, and graphs. Our descriptive statistics provide us with the tools we need to analyze and interpret our data in an accessible and appropriate manner. If we find the mean, median, or standard deviation for many data points, we can term this as an example of descriptive statistics.
A sample data set used for inferential statistics estimates the behaviour of the broader population that the sample represents. Inferential statistics aims to generalize a sample’s results to the population.
Based on the sample features, it calculates the probability that they will appear in the population. Probability theory is the most often employed method for hypothesis testing and analysis of variances.
Primary Distinctions Between Descriptive and Inferential Statistics
- An important aspect of descriptive statistics is its focus on providing detailed information about a population. However, inferential statistics aims to extrapolate information about the entire population from a smaller subset of data.
- Descriptive statistics is concerned with the meaningful collection, organization, analysis, and presentation of data. However, a more advanced form of inferential statistics is used to identify patterns in the data, formulate hypotheses, and forecast the future.
- Descriptive statistics summarize a sample by explaining data that is already known. Meanwhile, inferential statistics attempt to extrapolate information about a population beyond the currently available information.
- Descriptive Statistics aids in organization, analysis, and presentation to present the data meaningfully. You can construct hypotheses and draw conclusions using inferential statistics based on your data.
To Sum Up…
You should use descriptive statistics when you want to describe something, while if you’re going to explain the likelihood of an event, use inferential statistics.
Data from descriptive statistics are displayed in tabular or graphical format, while those from inferential statistics are presented as probabilities.
When conducting a descriptive analysis, you can choose which variables to leave out of your calculations if you only want to see how the other group fared. Sampling errors must be accounted for to ensure accurate inferential statistics, which may require additional tests on a large group.
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Kultar Singh – Chief Executive Officer, Sambodhi