9.5 Additional Information and Full Hypothesis Test Examples

Hypothesis testing is a crucial tool in statistics, helping us make decisions about population parameters based on sample data. It involves setting up hypotheses, calculating test statistics, and interpreting p-values to draw conclusions about the population.

Understanding significance levels, p-values, and different types of tests is key. We'll explore how to conduct hypothesis tests for proportions, including steps for calculating test statistics and making decisions based on p-values. This knowledge forms the foundation for statistical inference.

Hypothesis Testing

Significance and p-value interpretation

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• Level of significance ($\alpha$) represents the probability of rejecting the null hypothesis when it is actually true (Type I error)
  • Common $\alpha$ values: 0.01, 0.05, 0.10
  • Smaller $\alpha$ indicates a more stringent test and higher threshold for rejecting the null hypothesis
• p-value represents the probability of obtaining a sample statistic as extreme as or more extreme than the observed statistic, assuming the null hypothesis is true
  • Reject the null hypothesis if p-value < $\alpha$
  • Fail to reject the null hypothesis if p-value ≥ $\alpha$
• Level of significance is determined before conducting the test, while p-value is calculated based on the sample data
  • Example: If $\alpha = 0.05$ and p-value = 0.02, reject the null hypothesis since 0.02 < 0.05
• The rejection region is the set of values for the test statistic that leads to rejecting the null hypothesis

Types of hypothesis tests

• Type of hypothesis test determined by the alternative hypothesis ($H_a$ or $H_1$)
• Left-tailed test: $H_a$ states the population parameter is less than a specific value
  • Critical region located in the left tail of the distribution
  • Example: $H_a: \mu < 100$ (population mean less than 100)
• Right-tailed test: $H_a$ states the population parameter is greater than a specific value
  • Critical region located in the right tail of the distribution
  • Example: $H_a: p > 0.5$ (population proportion greater than 0.5)
• Two-tailed test: $H_a$ states the population parameter is not equal to a specific value
  • Critical region split equally between the left and right tails of the distribution
  • Example: $H_a: \mu \neq 75$ (population mean not equal to 75)

Hypothesis testing for proportions

• Steps for hypothesis testing with population proportions:

1. State the null and alternative hypotheses
   • Null hypothesis ($H_0$): population proportion ($p$) equals a specific value ($p_0$)
   • Alternative hypothesis ($H_a$): $p$ is less than, greater than, or not equal to $p_0$, depending on test type
2. Determine the level of significance ($\alpha$) and test type (left-tailed, right-tailed, or two-tailed)
3. Calculate the test statistic ($z$) using the sample proportion ($\hat{p}$), null proportion ($p_0$), and standard error ($\sqrt{\frac{p_0(1-p_0)}{n}}$)
   • $z = \frac{\hat{p} - p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}}$
4. Find the p-value using the z-score and standard normal distribution
   • Left-tailed test: p-value = $P(Z < z)$
   • Right-tailed test: p-value = $P(Z > z)$
   • Two-tailed test: p-value = $2 \cdot \min(P(Z < z), P(Z > z))$
5. Make a decision by comparing the p-value to the level of significance
   • Reject $H_0$ if p-value < $\alpha$
   • Fail to reject $H_0$ if p-value ≥ $\alpha$
6. Interpret the results in the context of the problem
   • Example: If testing whether a coin is fair ($H_0: p = 0.5$) and p-value = 0.03 with $\alpha = 0.05$, reject $H_0$ and conclude the coin is not fair

Additional Considerations in Hypothesis Testing

• Confidence interval: A range of values that likely contains the true population parameter, providing a measure of uncertainty
• Statistical power: The probability of correctly rejecting a false null hypothesis, which increases with larger sample sizes and effect sizes
• Effect size: A measure of the magnitude of the difference between groups or the strength of a relationship
• Degrees of freedom: The number of independent observations in a dataset that are free to vary, affecting the shape of the sampling distribution