"Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability."
The study of mathematical and computational tools used to analyze and interpret biological data, and the estimation of the probability of research outcomes or their occurrence.
Descriptive Statistics: This involves organizing, summarizing, and presenting data in a meaningful way. It includes measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation).
Inferential Statistics: This is the process of drawing conclusions about a population based on a sample. It includes hypothesis testing, confidence intervals, and regression analysis.
Probability Theory: This is the study of random events and their outcomes. It includes the concepts of probability distributions, conditional probability, and Bayes' theorem.
Random Variables: This is a quantity that takes on different values randomly. It includes discrete and continuous random variables and their probability distributions.
Central Limit Theorem: This is a theorem that states that as the sample size increases, the sample mean will approach a normal distribution.
Hypothesis Testing: This is a process to evaluate the evidence provided by a sample in order to draw inferences about a population. It involves determining the level of statistical significance and rejecting or accepting a null hypothesis.
Confidence Intervals: This is a range of values that are likely to contain a population parameter with a certain level of confidence.
Regression Analysis: This is the process of estimating the relationship between one or more independent variables and a dependent variable. It includes linear regression, logistic regression, and multiple regression.
Sampling Techniques: This is the process of selecting a representative subset of a population for analysis. It includes random sampling, stratified sampling, and cluster sampling.
Experimental Design: This is the process of designing scientific experiments to minimize bias and maximize the statistical power of the results.
Time Series Analysis: This is a statistical technique for analyzing data over time. It includes trend analysis, seasonality, and forecasting.
Multivariate Analysis: This is the analysis of data with multiple variables. It includes principal component analysis, factor analysis, and cluster analysis.
Survival Analysis: This is a statistical technique for analyzing data in which the outcome variable is time to an event like death or failure. It includes Kaplan-Meier curves, Cox proportional hazards models.
Bayesian Statistics: This is an approach to statistics in which probabilities are assigned to hypotheses and updated as new data become available.
Machine Learning: This is a field of computer science that involves teaching computers to learn from data, without being explicitly programmed. It includes supervised and unsupervised learning.
Descriptive statistics: This refers to the data analysis techniques used to describe the properties and features of large sets of data. It may include measures like mode, median, and mean, etc.
Inferential statistics: This type of statistics helps to generalize and draw conclusions about the population from sample observations.
Probability distributions: It refers to the mathematical representation of the probabilities of different events occurring in a statistical experiment. It can be used to predict the likelihood of a particular observation.
Hypothesis testing: This is a framework that allows the researcher to make inferences about the population parameters based on the sample data.
Regression analysis: This is a statistical method that measures the relationship between one dependent variable and one or more independent variables.
Bayesian statistics: This approach involves the use of prior knowledge and probability theory to model and represent the uncertainty present in the analysis.
Time-series analysis: This statistical tool is used to study the behavior of variables over time.
Survival analysis: This type of analysis is used to examine the effect of different variables on the outcome of an event which could be death or survival.
Machine learning techniques: Artificial intelligent models and techniques are widely used in bioinformatics to predict the behavior of biological systems and entities.
Network analysis: It provides a way to study the interactions between genes, proteins, and other biological entities to understand their behavior and function.
"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'."
"Using a model for prediction is referred to as inference (instead of prediction)."
"Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability."
"Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."
"Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data."
"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'."
"Using a model for prediction is referred to as inference (instead of prediction)."
"Statistical inference is the process of using data analysis to infer properties of an underlying distribution of probability."
"Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."
"Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data."
"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'."
"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."
"Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates."