In the world of statistics, there are two categories you should know. Descriptive statistics and inferential statistics are both important. Each one serves a purpose.
Inferential statistics look at the relationship between several variables present in a sample. These statistics will predict the future of variables. Sometimes they generalize about larger groups of people. They tell us what is happening.
These statistics interpret the data for us. This allows social scientists to view patterns. They can make sense of the information. They also use complex mathematics. This is the core difference between inferential and descriptive statistics.
How to Use Inferential Statistics
Inferential statistics examine relationships between variables in a sample. The statistics help people make predictions, or inferences, about a larger population. Scientists may use these kinds of statistics as a more affordable way to measure groups based on small samples so that it can later be applied to a large population.
For example, if you wanted to know the exact age at which each person in the country went on their first date, you probably wouldn't be able to ask everybody. Instead, you would need to find a sample size and draw conclusions based on the sample.
Inferential statistics is all about relationships and quantitative analysis. You can use inferential statistics to create logistic regression analysis and linear regression analysis.
Descriptive statistics describe and summarize data. Examples include numerical measures, like averages and correlation. Standard deviation is another descriptive statistic.
Descriptive statistics explain only the population you are studying. Scientists cannot use the information to generalize other groups. There are two types of descriptive statistics: measures of spread and measures of central tendency.
Measures of Spread
A measure of spread shows the distribution of a data set. The measure of spread also shows the relationship between each data point. A measure of spread includes the range, quartiles, variance, frequency distribution and mean absolute deviation.
We show measures of spread in different ways. For example, you can show a measure of spread on a bar chart, table or histogram. These charts help people interpret trends in data.
Measures of Central Tendency
Measures of central tendency are another form of descriptive statistics. The measure of central tendency reveals data trends. It includes the mean, median and mode. Each of these figures tell us something about the data.
For example, the mode is the most common value the data shows. The mode can tell you the age at which most people graduate from high school, for instance.
The media is the middle range of a data set. It can give us information about the set of ages in which people typically get their first job.
Finally, the mean is the average of the data. You can add up each piece of data and then divide that figure by the number of data pieces. You can use the mean to determine the average age at which people begin college, for instance.