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- Population: The entire group that you're interested in making conclusions about. For example, if you're studying the effects of a drug, the population might be all people with a certain disease.
- Sample: A subset of the population used to conduct the study. Ideally, the sample is representative of the population, allowing the results to be generalized.
- Variable: Any characteristic, number, or quantity that can be measured or counted. Variables may be classified as categorical (e.g., gender, color) or numerical (e.g., height, weight).
- Descriptive Statistics: Statistics that summarize the data collected from a sample. Common examples include mean (average), median (middle value), and mode (most frequent value).
- Inferential Statistics: Techniques used to draw conclusions about the population based on sample data. This includes estimating population parameters and testing hypotheses.
- Confidence Interval: A range of values, derived from the sample statistic, that is likely to contain the value of an unknown population parameter. For example, a 95% confidence interval means you can be 95% certain the interval contains the true parameter.
- P-value: The probability of obtaining an effect at least as extreme as the one in your sample data, assuming the null hypothesis is true. A low p-value (typically ≤ 0.05) indicates that the observed data are unlikely under the null hypothesis, suggesting a significant effect.
- Null Hypothesis (H0): The default position that there is no difference or no effect. In hypothesis testing, the goal is often to gather evidence to reject the null hypothesis.
- Alternative Hypothesis (H1): The hypothesis that there is a difference or effect. If the null hypothesis is rejected, the alternative hypothesis is supported.
- Type I Error: Incorrectly rejecting the null hypothesis when it is true (false positive).
- Type II Error: Failing to reject the null hypothesis when it is false (false negative).
- Standard Deviation: A measure of the amount of variation or dispersion in a set of values. A low standard deviation means that the values tend to be close to the mean, while a high standard deviation means they are spread out over a wider range.
- Regression Analysis: A statistical method for examining the relationship between two or more variables. For example, it can be used to understand how changes in one variable (independent) predict changes in another (dependent).
- Correlation: A measure of the relationship between two variables, ranging from -1 to 1. A correlation of 1 indicates a perfect positive relationship, -1 indicates a perfect negative relationship, and 0 indicates no relationship.
- 作者:现代数学启蒙
- 链接:https://www.math1234567.com/article/statisticsjargons
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