"The discipline that concerns the collection, organization, analysis, interpretation, and presentation of data."
The collection, analysis, interpretation, presentation, and organization of data.
Descriptive statistics: This refers to the tools and methods used to describe or summarize data, usually involving measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation, range).
Probability theory: This is the branch of mathematics that deals with the study of random events, such as coin flips or card draws, and assigns probabilities to their outcomes.
Inferential statistics: This refers to the branch of statistics concerned with making inferences or predictions about a population based on a sample of data. It includes methods like hypothesis testing, confidence intervals, and regression analysis.
Sampling techniques: This refers to the methods used to select a subset of individuals or items from a larger population in order to conduct statistical analyses.
Data collection: This refers to the process of gathering data through various methods, such as surveys, experiments, or observational studies.
Correlation and causation: This refers to the relationship between two variables, where correlation means that they are related, while causation implies that changes in one variable directly cause changes in the other.
Probability distributions: This refers to the various mathematical functions that describe the probabilities of different outcomes in a random process, such as the normal distribution or the Poisson distribution.
Confidence intervals: This is a range of values calculated from a sample of data that is likely to include the true value of a population parameter with a specified degree of confidence.
Hypothesis testing: This is a method used to test a hypothesis or claim about a population parameter using sample data, where the null hypothesis is assumed to be true until proven otherwise.
Regression analysis: This is a statistical tool used to model the relationship between a dependent variable and one or more independent variables, often used for prediction or understanding the underlying relationships between variables.
Experimental design: This refers to the process of planning and executing experiments in a way that minimizes bias and maximizes the ability to draw conclusions about cause and effect relationships between variables.
Statistical software: This refers to the various computer programs and applications used for data analysis, such as SPSS, SAS, R, or Excel.
Descriptive Statistics: It involves the collection and summarization of data pertaining to a particular population or sample.
Inferential Statistics: It entails using data to make inferences or predictions about an entire population, based on collected sample data.
Probability theory: It is the study of randomness and uncertainty in events, and the likelihood or chance of their occurrence.
Regression analysis: It involves predicting a continuous variable, such as predicting stock market trends or sales projections based on historical data.
Time series analysis: It provides a way of analyzing and predicting trends and patterns in time-based data, such as weather conditions, sales, or stock market fluctuations.
Multivariate analysis: It involves understanding the relationships between multiple variables and the dependency of one variable on the other.
Design of experiments: It is concerned with designing and analyzing controlled studies to identify causal relationships between variables.
Statistical modeling: It involves developing models to simulate or predict the behavior of complex systems or phenomena.
Non-parametric statistics: It is a method that does not assume any specific statistical distribution, and uses techniques such as rank tests and permutation tests.
Bayesian and decision theory: It involves making decisions based on predictions and probability distributions, which can be calculated using Bayesian inference or decision theory models.
Survey methodology: It is concerned with the design and implementation of surveys to collect data from a specific population and analyze the results.
Data mining and machine learning: It involves using algorithms and computational techniques to find patterns and insights in large and complex datasets.
Quality control and quality assurance: It involves the application of statistical techniques to monitor and improve the quality of products, processes, and services.
Causal inference: It involves identifying and estimating causal relationships between variables and controlling for confounding factors that may affect the results.
Social statistics: It is the application of statistical techniques to social science research, such as demographic, economic, and political studies.
"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)."