"A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information."
Methods used to summarize and describe data, such as measures of central tendency, variability, and frequency distributions.
Central Tendency: A measure that represents the midpoint or center of a data set.
Variability: A measure that shows how dispersed or spread out the data is.
Skewness: A measure of the asymmetry of the probability distribution of a real-valued random variable.
Kurtosis: A measure of the peakedness or flatness of a probability distribution.
Measures of Association: A measure of the strength and direction of the relationship between two variables.
Measures of Dispersion: A measure of the degree of variability or spread of a set of data values.
Probability: The study of the likelihood of an event occurring.
Hypothesis Testing: The process of testing a hypothesis to determine whether it is supported by data.
Confidence Intervals: A range of values in which a population parameter is estimated to lie.
Normal Distribution: A probability distribution that is symmetric with a bell-shaped curve.
Sampling Distributions: A probability distribution that is a theoretical distribution of a statistic obtained from a large number of samples.
Power and Sample Size: The ability to detect a significant effect in a statistical test.
Correlation and Regression Analysis: Methods used to measure the strength and direction of the relationship between two variables.
Data Visualization: Techniques used to display and represent data visually.
Data Cleaning and Preprocessing: The process of removing or correcting errors and inconsistencies in a data set.
Measures of central tendency: Statistics used to describe the middle of a set of data, including mean, median, and mode.
Measures of dispersion: Statistics used to measure the variation of a set of data, including range, standard deviation, and variance.
Skewness: A measure of the degree of asymmetry of a distribution.
Kurtosis: A measure of the degree of peakedness or flatness of a distribution.
Correlation: A measure of the degree of association between two variables.
Regression: A statistical tool used to find the relationship between two or more variables.
Percentiles: A measure used to divide a set of observations into equal parts based on their rank.
Z-scores: A measure of the distance between an observation and the mean of a set of data.
Probability distributions: A mathematical function that describes the likelihood of different outcomes occurring in a random event.
Confidence intervals: A range of values used to estimate the true value of a population parameter.
Hypothesis testing: A statistical tool used to test a hypothesis about a population parameter.
Chi-square test: A test used to compare the observed and expected frequencies of categorical data.
ANOVA: A statistical tool used to test whether there are significant differences between the means of two or more groups.
Time series analysis: A statistical tool used to analyze data collected over time to identify patterns and trends.
"Descriptive statistics is distinguished from inferential statistics (or inductive statistics) by its aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent."
"This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently nonparametric statistics."
"Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented."
"For example, in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics."
"Such as the average age, the proportion of subjects of each sex, the proportion of subjects with related co-morbidities, etc."
"Measures of central tendency include the mean, median and mode."
"Measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness."
"Measures of central tendency quantitatively describe or summarize the typical or average value within a data set."
"The mean is an important measure of central tendency as it represents the arithmetic average of the data."
"The median represents the middle value in the data set when it is arranged in ascending order, whereas the mean is affected by extreme values."
"The mode is the value that appears most frequently in the data set."
"Measures of variability provide information about the range or extent of spread within a data set."
"The standard deviation measures the average distance between each data point and the mean."
"The minimum and maximum values give insights into the range of values observed in the data set."
"Kurtosis measures the distribution's departure from normality, particularly focusing on the tails or extreme values."
"Skewness indicates the asymmetry of the data distribution, specifically if it is skewed to the left or right."
"Descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently nonparametric statistics."
"Descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent."
"Descriptive statistics are developed to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent."