Together, these powerful statistical techniques are the foundational bedrock on which data analytics is built. Inferential statistics focus on making generalizations about a larger population based on a representative sample of that population. Because inferential statistics focuses on making predictions (rather than stating facts) its results are usually in the form of a probability. Descriptive statistics help us to summarize and understand the data we have, inferential statistics help us to make predictions and inferences about larger populations based on that data.
Mehrnaz holds a Masters in Data Analytics and is a full time biostatistician working on complex machine learning development and statistical analysis in healthcare. She has experience with AI and has taught university courses in biostatistics and machine learning at University of the People. If all samples show similar results and we know that they are representative and random, we can generalize that the vaccine will have the same effect on the population at large. On the flip side, if one sample shows higher or lower efficacy than the others, we must investigate why this might be.
This can involve measures of central tendency like the mean, median, or mode, which give a sense of the “average” data point. It may also include measures of variability such as the range, standard deviation, or variance, which provide insights into the spread of the data. Descriptive statistics condense a large dataset into a simplified but informative snapshot, giving a clear picture of the dataset without drawing conclusions beyond what is immediately apparent. The tools used in descriptive and inferential statistics are measures of central tendency, measures of dispersion, hypothesis testing, and regression analysis. Descriptive statistics summarize and describe the main features of a dataset through measures like mean, median, and standard deviation, providing a quick overview of the sample data.
For instance, the mean (or average) of a sample will rarely match the mean of the full population, but it will give you a good idea of it. For this reason, it’s important to incorporate your error margin in any analysis (which we cover in a moment). This is why, as explained earlier, any result from inferential techniques is in the form of a probability. Both descriptive and inferential statistics play integral roles in data analysis.
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Descriptive statistics are used extensively to provide a summary of any given dataset. For example, in the field of economics, descriptive statistics would include measures of GDP or unemployment rates. In business, it would include the number of sales per department over the last quarter.
Inferential statistics involve hypothesis testing, correlations, regressions, confidence intervals, chi-square tests, t-tests, ANOVA (Analysis of Variance), etc. While descriptive statistics summarize the data, inferential statistics make predictions and draw conclusions about a larger population. In contrast, inferential statistics allows analysts to extrapolate and make predictions or hypotheses about a larger population based on their sample data. It uses complex mathematical models to estimate parameters and to test hypotheses.
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Descriptives are also critical to the exploratory data analysis stage of any large statistical or data-driven project. What we’ve described here is just a small selection of a great many inferential techniques that you can use within data analytics. However, they provide a tantalizing taste of the sort of predictive power that inferential statistics can offer. We can use descriptive statistics to describe both an entire population or an individual sample.
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They cannot be used to make predictions or provide support for statistical hypotheses. Correlation analysis, meanwhile, measures the degree of association between two or more datasets. Unlike regression analysis, correlation does not infer cause and effect. For instance, ice cream sales and sunburn are both likely to be higher on sunny days—we can say that they are correlated. You can learn more about correlation (and how it differs from covariance) in this guide.
Yes, hypothesis tests such as z test, f test, ANOVA test, and t-test are a part of descriptive and inferential statistics. Hypothesis testing along with regression analysis specifically fall under inferential statistics. However, our sample is unlikely to provide a perfect estimate for the population. Fortunately, we can account for this uncertainty by creating a confidence interval, which provides a range of values that we’re confident the true population parameter falls in. Interested in building a career path within the dynamic world of data analytics?
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- There is often a descriptive phase where the basic characteristics of the data are explored and understood.
- These provide further insights into the distribution and the nature of the data.
- Understanding the difference between descriptive vs. inferential statistics is crucial in today’s data-driven world.
- It also includes methods of dispersion (such as the range, variance, and standard deviation) that describe how spread out the data is around those measures of central tendency.
- Descriptive statistics is primarily concerned with the presentation of data in a meaningful way, which includes graphical representation and numerical analysis.
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One common type of table is a frequency table, which tells us how many data values fall within certain ranges. Using descriptive statistics, we could find the average score and create a graph that helps us visualize the distribution of scores. Mean, median, mode, range, variance, standard deviation, histograms, box plots, etc. Master calculating residuals in regression analysis to refine model accuracy and gain deeper data insights.
Descriptive statistics does not draw conclusions or make predictions beyond the data at hand. Hypothesis testing is a fundamental technique in inferential statistics used to make descriptive vs inferential statistics decisions or draw conclusions about a population parameter based on sample data. Common statistical tests for hypothesis testing include t-tests, chi-square tests, ANOVA (Analysis of Variance), and z-tests. The purpose of descriptive statistics is to reduce a complex data set to a more straightforward summary. This involves measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation).
Common types of regression analysis include linear, logistic, polynomial, and multiple regression. Descriptive statistics provide valuable insights but do not allow for predictions about broader populations, which is where inferential statistics come in. Descriptive statistics are often the preliminary data analysis stage and form the foundation for inferential statistics. Moreover, descriptive statistics also encompass measures of position (percentiles, quartiles) and shape (skewness, kurtosis).