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Hypothesis testing uses data to evaluate assumptions about population parameters, aiding in informed decision-making across various fields.
The null hypothesis represents no effect or difference, while the alternative suggests a significant effect, guiding the direction of testing.
A significance level, often at 0.05, sets the threshold for rejecting the null hypothesis, balancing the risk of incorrect conclusions.
The p-value quantifies the probability of observing your data if the null hypothesis is true, crucial for making statistical decisions.
Parametric tests require data to follow a specific distribution, whereas non-parametric tests are more flexible with fewer assumptions.
T-tests assess differences between means, with independent samples t-test for separate groups and paired samples t-test for related groups.
Demystify statistics and discover how to analyze your data efficiently.
Chi-squared tests assess associations between categorical variables, while ANOVA compares means across multiple groups.
Pearson’s Correlation quantifies linear relationships between variables, while regression models predict outcomes based on predictors.
Levene’s Test checks for equal variances, and Shapiro-Wilk tests for normal distribution, ensuring the reliability of further analysis.
Compare the p-value to the significance level to decide on rejecting the null hypothesis, integrating statistical evidence into conclusions.
Effect size quantifies the strength of relationships, while statistical power indicates the test's sensitivity to detect effects.
Confidence intervals provide a range for where the true parameter value lies, adding depth to hypothesis test results.
Access the full guide to enhance your statistical decision-making skills with hypothesis testing.