Statistics in data science involves collecting, analyzing, interpreting, presenting, and organizing data to make informed decisions and predictions.​

Hypothesis testing is a statistical method used to determine whether there is enough evidence to reject a null hypothesis in favor of an alternative hypothesis.

The p-value measures the strength of evidence against the null hypothesis. A lower p-value indicates stronger evidence in favor of the alternative hypothesis.​

This rule states that in a normal distribution:

• 68% of data falls within 1 standard deviation of the mean.
• 95% within 2 standard deviations.
• 99.7% within 3 standard deviations.

Statistical power is the probability that a study will detect an effect when there is an effect to be detected. 

Heteroscedasticity refers to a situation in which the variance of errors in a regression model is not constant across all levels of an independent variable. ​

Parametric tests assume that the data follows a specific distribution, such as normal distribution, while non-parametric tests do not make such assumptions.

Effect size is a quantitative measure of the magnitude of a phenomenon. In statistics, it helps you understand how much impact a treatment or variable has, beyond just knowing whether the result is statistically significant.

Cross-validation is a technique used to assess the performance of a statistical model by partitioning the data into subsets, training the model on some subsets, and testing it on others. 

Confidence intervals for proportions estimate the range in which the true population proportion lies with a certain level of confidence. ​

• Correlation measures the strength and direction of the linear relationship between two variables.
• Covariance indicates the direction of the linear relationship but not the strength.​

Regression analysis is a statistical technique used to model and analyze the relationships between a dependent variable and one or more independent variables.

Bayesian statistics is an approach to statistics which considers probability as a measure of belief or certainty rather than a frequency, updating beliefs with new evidence.

These are measures of central tendency:

• Mean is the average of all data points.
• Median is the middle value when data points are ordered.
• Mode is the most frequently occurring value.

Sampling bias occurs when certain members of a population are systematically more likely to be selected for a sample than others, leading to inaccurate conclusions.

• Type I error: Incorrectly rejecting a true null hypothesis (false positive).
• Type II error: Failing to reject a false null hypothesis (false negative).​

A confidence interval provides a range of values that is likely to contain the population parameter with a certain level of confidence, such as 95%.

Standard deviation measures the amount of variation or dispersion in a set of data points. A low standard deviation indicates that the data points tend to be close to the mean.​

Descriptive statistics summarize and describe the features of a dataset, while inferential statistics make predictions or inferences about a population based on a sample.

Techniques for handling missing data include imputation, deletion, prediction models, and flagging. ​

Sampling is the process of selecting a subset of individuals from a population to estimate characteristics of the whole population.​

The central limit theorem states that the distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution.

Statistics provides foundational methods for analyzing data, testing hypotheses, and making predictions, which are essential in data science workflows.​

Statistical significance indicates whether the observed effect in a study is likely due to chance or if it reflects a true relationship.

Outlier detection involves identifying data points that differ significantly from the rest of the data, which may indicate variability in the data or errors.