- "Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability."
Involves the use of quantitative methods to identify patterns and relationships within data and draw valid conclusions from them. Common methods include descriptive statistics and inferential statistics.
Descriptive statistics: This involves applying statistical measures such as mean, median, mode, and standard deviation to describe and analyze data.
Inferential statistics: This involves making inferences about a population based on a sample of data gathered from that population.
Probability theory: This deals with the study of probabilities and their application in analyzing medical research data.
Probability distributions: This involves understanding the different types of probability distributions such as the normal distribution, Poisson distribution, binomial distribution, etc.
Hypothesis testing: This involves testing a hypothesis by analyzing a sample of data using statistical procedures.
Regression analysis: This involves analyzing the relationship between two or more variables to understand how they influence one another.
Analysis of variance (ANOVA): This involves analyzing the differences between means of two or more groups to see if they are statistically significant.
Survival analysis: This involves analyzing the time it takes for an event to occur in medical research data.
Correlation analysis: This involves analyzing the relationship between two continuous variables to determine the strength and direction of their association.
Time series analysis: This involves analyzing data collected over time to identify patterns and trends.
Non-parametric statistics: This involves analyzing data that does not meet the assumptions of parametric statistics.
Multivariate analysis: This involves analyzing data with multiple independent variables to determine how they are related to a dependent variable.
Factor analysis: This involves identifying underlying factors that explain the observed correlations between multiple variables.
Sampling methods: This involves identifying appropriate sampling methods for medical research studies to ensure the sample represents the population adequately.
Power analysis: This involves determining the appropriate sample size for a study to achieve the desired level of statistical power.
Reporting results: This involves communicating study findings effectively through written and oral presentations, tables, graphs, and statistical language.
Descriptive statistics: The analysis of numerical data to summarize it using measures like mean, median, mode, and standard deviation.
Inferential statistics: An analytical method used to make generalizations about a larger group based on a randomized sample a smaller group.
Hypothesis testing: Statistical analysis whereby the null hypothesis and an alternative/hypothesis is tested, in order to demonstrate a statistically significant difference or lack of difference between the two conditions.
Regression analysis: A statistical method that identifies the relationship between a dependent variable and one or more independent variables.
Multivariate analysis: A statistical technique that examines multiple variables simultaneously to identify relationships between them.
Time-series analysis: The exploration of trends over time using statistical methods such as ARIMA or regression.
Survival analysis: Statistical methods used to analyze and model the occurrence of an event.
Meta-analysis: A statistical method used to collate and analyze data from a variety of studies in order to identify patterns and reach more powerful conclusions.
Bayesian analysis: A statistical approach aimed at updating probabilities or estimates with information drawn systematically from new data.
Cluster analysis: A technique to identify subgroups or clusters of individuals or items among a larger population.
Factor analysis: A statistical method used to analyze the interrelationships among multiple variables in order to reduce their number into a smaller group of underlying factors.
Discriminant analysis: A statistical method used to determine whether a given set of independent variables can identify a defined condition of dependent or outcome variable(s).
Network analysis: A statistical method used to analyze the interrelationships among multiple variables in a network of connections.
Machine Learning: A group of statistical algorithms used to identify patterns in data and make predictions. The techniques include artificial neural networks, Bayesian networks, decision trees, and support vector machines.
Structural equation modeling (SEM): A statistical technique used to model complex relationships among multiple variables.
Item response theory (IRT): A statistical modeling approach used to estimate the latent traits of individuals based on their responses to items in a test or survey.
Path analysis: A statistical method used to model interdependencies among multiple variables in a directed acyclic graph.
Design of experiment (DOE): A statistical approach to optimize the experimental conditions of a study, to minimize potential bias, confounding or replicate results in a given timeframe, resource or budget.
- "Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."
- "It is assumed that the observed data set is sampled from a larger population."
- "Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "In this context, inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."
- "Inferential statistical analysis infers properties of a population, while descriptive statistical analysis is solely concerned with properties of the observed data."
- "Inferential statistical analysis infers properties of a population, for example by... deriving estimates."
- "It is assumed that the observed data set is sampled from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "Inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."
- "Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."
- "Descriptive statistics is solely concerned with properties of the observed data."
- "Descriptive statistics... does not rest on the assumption that the data come from a larger population."
- "It is assumed that the observed data set is sampled from a larger population."
- "In machine learning, the term inference is sometimes used instead to mean 'make a prediction, by evaluating an already trained model'."
- "Inferring properties of the model is referred to as training or learning (rather than inference)."
- "Using a model for prediction is referred to as inference (instead of prediction)."