Difference Between Descriptive and Inferential Statistics
Descriptive vs Inferential Statistics
Statistics is one of the most important parts of research today considering how they organize data into measurable forms. However, some students are usually confused between descriptive and inferential statistics, making it hard for them to distinguish the best option to use in their research.
If you look closely, the difference between descriptive and inferential statistics is already pretty obvious in their given names. “Descriptive” describes data while “inferential” infers or allows the researcher to arrive at a conclusion based on the collected information.
For example, you are tasked to research about teenage pregnancy in a certain high school. Using both descriptive and inferential statistics, you will be researching the number of teenage pregnancy cases in the school for a specific number of years. The difference is that with descriptive statistics, you are merely summarizing the collected data and, if possible, detecting a pattern in the changes. For example, it can be said that for the past five years, the majority of teenage pregnancies in X High School happened to those enrolled in the third year level. There’s no need to predict that on the sixth year, the third year level would still be the ones that would have the larger case of teenage pregnancies. Conclusions as well as predictions are only done for inferential statistics.
The principle of describing or concluding also applies to the data or the collected information of the researcher. Referring back to our earlier example about teenage pregnancies, descriptive statistics is only restricted to the population they are describing. To put it simply, the data collected on X High School regarding teenage pregnancy is ONLY applicable to the said institution.
In inferential statistics, X High School could just be a sample of the target population. Let’s say you are aiming to find out the status of teenage pregnancies in New York. Since it would be impossible to collect data from each high school in New York, X High School will then act as a sample that would reflect or represent all high schools in New York City. Of course, this usually means that a margin of error is present since one sample is not enough to represent the whole population. This rate of possible error is also taken into account when analyzing the data. Using the various calculations like; mean, median, and mode, researchers would be able to describe or examine data and achieve what they want through the process.
Statistics is largely important in today’s industry – especially the inferential type – mainly because it provides information that could help individuals make decisions in the future. For example, launching inferential statistics on the rate of population growth in a particular city could serve as a basis for a business to decide whether or not to set up shop in the place. The fact that it also utilizes numbers to arrive at conclusions enhances the accuracy of the research as well as the understandability of the data.
Statistic results are often shown through various models from graphs to charts. To increase accuracy, researchers also take into account various factors that could affect their population into consideration and translate it into numerical data. This way, the probability of error is minimized and a thoroughly summarized view of the case is achieved.
1.Descriptive statistics merely “describes” research and does not allow for conclusions or predictions.
2.Inferential statistics makes it possible for the researcher to arrive at a conclusion and predict changes that may occur regarding the area of concern.
3.Descriptive statistics usually operate within a specific area that contains ALL the target population.
4.Inferential statistics usually takes a sample of a population especially if the population is too big to conduct research.
Search DifferenceBetween.net :
Email This Post : If you like this article or our site. Please spread the word. Share it with your friends/family.