Descriptive statistics is a branch of statistics that deals with summarizing and describing the main features of a dataset. It provides methods for organizing, visualizing, and presenting data meaningfully and informally. Descriptive statistics describe the characteristics of the data set under study without generalizing beyond the analyzed data. Hypothesis testing allows analysts to make statistically-based decisions about a larger population based on sample data. Examples include hypothesis testing procedures such as regression analysis, ANOVA, t-tests, chi-square, and correlation tests.
Then, we can use the mean height of the plants in the sample to estimate the mean height for the population. For example, we might be interested in the mean height of a certain plant species in Australia. To visualize the distribution of test scores, we can create a histogram – a type of chart that uses rectangular bars to represent frequencies. Common types of graphs used to visualize data include boxplots, histograms, stem-and-leaf plots, and scatterplots.
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Difference between Descriptive and Inferential statistics
Insights from this phase then drive the inferential phase of statistical analysis. Regression analysis aims to determine how one dependent (or output) variable is impacted by one or more independent (or input) variables. For example, to predict future sales of sunscreen (an output variable) you might compare last year’s sales against weather data (which are both input variables) to see how much sales increased on sunny days. Once you’ve determined the sample size, you can draw a random selection. You might do this using a random number generator, assigning each value a number and selecting the numbers at random. Or you could do it using a range of similar techniques or algorithms (we won’t go into detail here, as this is a topic in its own right, but you get the idea).
Descriptive and inferential statistics are both statistical procedures that help describe a data sample set and draw inferences from the same, respectively. The ScienceStruck article below enlists the difference between descriptive and inferential statistics with examples. A clear benefit of inferential statistics is that they allow for predictions and generalizations using a sample dataset. Interpreting the results of inferential statistics tests can be difficult. The validity and accuracy of the results also depends strongly on the sample size of the available dataset.
Master the Student’s t-test to accurately compare population means, ensuring valid conclusions in your research. Explore the assumptions and applications of the Chi-Square Test of Independence, a crucial tool for analyzing categorical data in various fields. It’s a method of summarizing data, offering clear insights into the sample. ‘Biostatistics is the discipline concerned with the treatment and analysis of numerical data derived from biological, biomedical, and health-related studies’ (Gerstman B.B., 2015). Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data.
Chi-square tests can determine if there is an association between two categorical variables. There are many other techniques as well, such as regression analysis, factor analysis, and survival analysis. Inferential statistics are techniques that allow statisticians to use data from a sample to make inferences or predictions for a larger population. Central to inferential statistics is the idea of hypothesis testing where data from a subset of the population is used to provide probabilistic support for a hypothesis about the larger population. Confidence intervals are used to estimate certain parameters for a measurement of a population (such as the mean) based on sample data.
Unsurprisingly, the accuracy of inferential statistics relies heavily on the sample data being both accurate and representative of the larger population. If you’ve ever read news coverage of scientific studies, you’ll have come across the term before. Using the data gathered from a small group (a sample), they infer and make predictions about the larger group (the population).
Common Forms of Inferential Statistics
Random sampling from representative groups allows us to draw broad conclusions about an overall population. This the entire group that you wish to draw data from (and subsequently draw conclusions about). This is useful for helping us gain a quick and easy understanding of a data set without pouring over all of the individual data values. Along with using an appropriate sampling method, it’s important to ensure that the sample is large enough so that you have enough data to generalize to the larger population. A frequency table is particularly helpful if we want to know what percentage of the data values fall above or below a certain value. For example, suppose the school considers an “acceptable” test score to be any score above a 75.
This is often facilitated through graphical representations, tables, or numerical measures. The bigger your sample size, the more representative it will be of descriptive vs inferential statistics the overall population. Drawing large samples can be time-consuming, difficult, and expensive.
- It provides simple, straightforward summaries of the sample and its measures, ensuring a comprehensive yet simplified understanding of the data set.
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- Moreover, descriptive statistics also encompass measures of position (percentiles, quartiles) and shape (skewness, kurtosis).
- This program equips you with essential statistical fundamentals, including the disparities between descriptive and inferential statistics.
- For this reason, it’s important to incorporate your error margin in any analysis (which we cover in a moment).
However, their objectives, methodologies, and the nature of the insights they provide are fundamentally different. In summary, here is a chart showing the main differences between descriptive and inferential statistics and some questions to test your understanding. Descriptive statistics is used to describe and organize data while inferential statistics draw conclusions about the population from samples by using analytical tools. The purpose of descriptive and inferential statistics is to analyze different types of data using different tools. Descriptive statistics helps to describe and organize known data using charts, bar graphs, etc., while inferential statistics aims at making inferences and generalizations about the population data. Descriptive statistics is used to describe data and inferential statistics is used to make predictions.
They provide a way to summarize, visualize, and comprehend an extensive data set without resorting to complex calculations or analyses. Inferential statistics should be used when the goal is to make predictions about a population or if a hypothesis about the data is being tested. It can also provide a more robust understanding of the relationships between variables. Choosing between descriptive and inferential statistics depends on the research question, the nature of the data, and the objectives of the analysis. For example, let’s say you’ve measured the tails of 40 randomly selected cats.
Because they are merely explanatory, descriptive statistics are not heavily concerned with the differences between the two types of data. Descriptive statistics are used to describe the characteristics or features of a dataset. The term “descriptive statistics” can be used to describe both individual quantitative observations (also known as “summary statistics”) as well as the overall process of obtaining insights from these data. In a nutshell, descriptive statistics focus on describing the visible characteristics of a dataset (a population or sample). In a nutshell, descriptive statistics aims to describe a chunk of raw data using summary statistics, graphs, and tables.
Descriptive vs inferential statistics: an overview
This provides valuable but surface-level insight into the data collected. Once the data have been arranged in a table, descriptive statistics also makes use of graphics. Practically, both methods are combined in most statistical applications. There is often a descriptive phase where the basic characteristics of the data are explored and understood.