"The discipline that concerns the collection, organization, analysis, interpretation, and presentation of data."
The study of methods for collecting, analyzing and interpreting data.
Descriptive Statistics: This involves summarizing and describing data using measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation).
Inferential Statistics: This involves using data from a sample to make predictions or generalizations about a larger population.
Probability Theory: This involves understanding the likelihood or chance of an event occurring in a given situation.
Statistical Inference: This involves making conclusions or predictions about a population based on sample data.
Hypothesis Testing: This involves testing a claim or hypothesis about a population using sample data.
Sampling Techniques: This involves selecting a representative sample from a population in order to make inferences about the population as a whole.
Regression Analysis: This involves examining the relationship between two or more variables and using that relationship to predict future outcomes.
Analysis of Variance (ANOVA): This involves comparing the means of two or more groups to determine if they are statistically different from each other.
Experimental Design: This involves designing and conducting experiments in order to make causal inferences about the relationship between variables.
Time Series Analysis: This involves analyzing data over time to identify patterns and trends.
Data Visualization: This involves presenting data in visual form (e.g. graphs, charts) in order to better understand and communicate findings.
Bayesian Statistics: This involves using prior knowledge and beliefs about a population to update and revise our understanding of it based on new data.
Multivariate Statistics: This involves analyzing data that involves more than two variables simultaneously.
Nonparametric Statistics: This involves using statistical methods that do not rely on specific assumptions about the underlying distribution of the data.
Survival Analysis: This involves analyzing data on the time until an event occurs (e.g. time to failure, time to death).
Descriptive Statistics: This includes summarizing and describing data in different ways using graphs, averages, and other measures.
Inferential Statistics: This type involves making predictions and generalizations about a population based on a sample.
Correlation Analysis: This type involves measuring the association between two variables and determining the strength of the relationship.
Regression Analysis: This involves analyzing the relationship between one variable and one or more others.
Hypothesis Testing: This type involves testing a theory or idea about a population using data and statistical tests.
Time Series Analysis: This involves analyzing data that is collected over time to identify trends and patterns.
Bayesian Statistics: This involves using probability to make decisions and draw conclusions.
Survey Sampling: This involves selecting a sample for a study and ensuring that it is representative of the population.
Multivariate Statistics: This involves analyzing multiple variables simultaneously to identify relationships and patterns.
Experimental Design: This involves designing an experiment to test a theory or idea and analyzing the results.
"Collection, organization, analysis, interpretation, and presentation of data."
"Populations can be diverse groups of people or objects such as 'all people living in a country' or 'every atom composing a crystal'."
"Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole."
"Statisticians collect data by developing specific experiment designs and survey samples."
"An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation."
"Descriptive statistics" and "inferential statistics."
"Descriptive statistics summarize data from a sample using indexes such as the mean or standard deviation."
"Inferential statistics draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation)."
"Central tendency (or location)" and "dispersion (or variability)."
"The framework of probability theory, which deals with the analysis of random phenomena."
"A hypothesis is proposed for the statistical relationship between two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets."
"Type I errors (null hypothesis is falsely rejected giving a 'false positive')" and "Type II errors (null hypothesis fails to be rejected and an actual relationship between populations is missed giving a 'false negative')."
"Random (noise) or systematic (bias) errors" and "other types of errors (e.g., blunder, such as when an analyst reports incorrect units)."
"The presence of missing data or censoring may result in biased estimates."
"Obtaining a sufficient sample size" and "specifying an adequate null hypothesis."
"Inferential statistics are made under the framework of probability theory."
"When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples."
"Descriptive statistics are most often concerned with two sets of properties of a distribution: central tendency (or location) and dispersion (or variability)."
"Inferential statistics draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation)."