Examples of inferential and descriptive statistics

This blog is based on Descriptive and Inferential statistics. It never attempts to use a sample to reach a conclusion.

We are interested in understanding the distribution of test scores so we use the following descriptive statistics.

Examples of inferential and descriptive statistics. Descriptive statistics is the statistical description of the data set. Mean median mode variance and standard deviation. Inferential statistics is the drawing of inferences or conclusion based on a set of observations.

These observations had been described by the descriptive statistics. Example of Using Descriptive Statistics. The following example illustrates how we might use descriptive statistics in the real world.

Suppose 1000 students at a certain school all take the same test. We are interested in understanding the distribution of test scores so we use the following descriptive statistics. This tells us that the average test score among all 1000 students is 8213.

Inferential statistics involves studying a sample of data. The term implies that information has to be inferred from the presented data. A sample of the data is considered studied and analyzed.

Unlike descriptive statistics this data analysis can extend to a similar larger group and can be visually represented by means of graphic elements. Inferential Statistics Above we explore descriptive analysis and it helps with a great amount of summarizing data. The examples regarding the 100 test scores was an analysis of a population.

A population is a group of data that has all of the information that youre interested in using. Inferential statistics examples have no limit. They are available to facilitate us in estimating populations.

Its use is indeed more challenging but the efficiency that is presented greatly helps us in various surveys or research. Descriptive statistics and inferential statistics are data processing tools that complement each other. It makes our analysis become powerful and meaningful.

Inferential statistics are produced through complex mathematical calculations that allow scientists to infer trends about a larger population based on a study of a sample taken from it. Scientists use inferential statistics to examine the relationships between variables within a sample and then make generalizations or predictions about how those variables will relate to a larger population. When we use descriptive statistics it is useful to summarize our group of data using a combination of tabulated description ie tables graphical description ie graphs and charts and statistical commentary ie a discussion of the results.

We have seen that descriptive statistics provide information about our. Descriptive and Inferential Statistics 4 The Department of Statistics and Data Sciences The University of Texas at Austin click on the arrow button that will move those variables to the Variables boxFor example. Venkataraman 2015 uses a descriptive statistics to acquired data on ownership type and experiential quality measures data on control variables such as resident-to-patients ratio operating disproportionate share hospital adjustment estimated operating outlier payments as a percentage of the providers federal operating PPS payments case mix index CMI and location rural large urban or other urban and data on hospital.

Now let we use inferential statistics for this example of research. Above is the scatter plot of students height and their math score. The difference of descriptive statistics and inferential statistics are.

Difference of numbers of variables. The most common methodologies in inferential statistics are hypothesis tests confidence intervals and regression analysis. Interestingly these inferential methods can produce similar summary values as descriptive statistics such as the mean and standard deviation.

In brief Descriptive statistics analyze the big data with the help of charts and tables. It never attempts to use a sample to reach a conclusion. On the contrary in Inferential statistics researchers test the hypothesis.

Heshe studies the sample and reaches the conclusions of the population. This blog is based on Descriptive and Inferential statistics. We have seen that descriptive statistics provide information about our immediate group of data.

For example we could calculate the mean and standard deviation of the exam marks for the 100 students and this could provide valuable information about this group of 100 students. Example inferential statistics In the example above a sample of 10 basketball players was drawn and then exactly this sample was described this is the task of descriptive statistics. If you want to make a statement about the population you need the inferential statistics.

Descriptive Statistics Inferential Statistics. It gives information about raw data which describes the data in some manner. It makes inference about population using data drawn from the population.

It helps in organizing analyzing and to present data in a meaningful manner. It allows us to compare data make hypothesis and predictions. Descriptive statistics summarize and organize characteristics of a data set.

A data set is a collection of responses or observations from a sample or entire population. In quantitative research after collecting data the first step of statistical analysis is to describe characteristics of the responses such as the average of one variable eg age or the relation between two variables eg age and creativity.