"The discipline that concerns the collection, organization, analysis, interpretation, and presentation of data."
The discipline of collecting, analyzing, and interpreting numerical data to make informed decisions.
Introduction to Statistics: A basic overview of what statistics is, its relevance in various fields, and the different types of statistical analysis.
Descriptive Statistics: Methods used to summarize and describe data, such as measures of central tendency, variability, and frequency distributions.
Inferential Statistics: Methods used to make predictions and generalizations about a population based on a sample, such as hypothesis testing and confidence intervals.
Probability: The study of the likelihood of events occurring and the principles governing their occurrence, such as conditional probability and Bayes' theorem.
Sampling Techniques: Methods used to select a representative sample from a population for statistical analysis, such as simple random sampling and stratified sampling.
Correlation and Regression Analysis: Methods used to examine the relationship between two or more variables, such as linear regression and correlation coefficients.
Experimental Design: Methods used to design and conduct experiments, such as randomized controlled trials and factorial experiments.
Statistical Software: The use of statistical software programs for data analysis and visualization, such as SPSS, SAS, and R.
Data Visualization: Methods used to represent data graphically, such as histograms, scatterplots, and boxplots.
"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)."