Goss-Sampson (2020), Statistical Analysis in JASP Some concepts in frequentist statistics

Question 1

Null Hypothesis (H₀):

Answer 1

The null hypothesis represents a default position or statement of no effect,
difference, or relationship in the population.

Question 2

Alternative Hypothesis (H₁):

Answer 2

The statement that there is an effect, difference, or relationship in the population,
contrary to the null hypothesis

Question 3

P-Value:

Answer 3

• Definition: The probability of obtaining a test statistic at least as extreme as the one observed,
  assuming the null hypothesis is true.
• Interpretation:
• Low p-value (< 0.05): Evidence against H₀, suggesting an effect exists.
• High p-value (≥ 0.05): Insufficient evidence to reject H₀ .
• Important Note: A low p-value does not measure the size of an effect, just the strength of
  evidence against H₀.

Question 4

Confidence Interval (CI):

Answer 4

Definition: A range of values that is believed to encompass the true population parameter with
a specified level of confidence (e.g., 95%).

Question 5

Effect Size:

Answer 5

• Definition: Quantifies the magnitude of the observed effect, independent of sample size.
• Common metrics:
• Cohen’s d: Measures the difference between two means in terms of standard deviation.
• Pearson’s r: Quantifies the strength of a correlation

Question 6

Type I Error

Answer 6

(False Positive): Incorrectly rejecting the null hypothesis when it is true.

Question 7

Type II Error

Answer 7

(False Negative): Failing to reject the null hypothesis when it is false.

Question 8

Assumptions of Frequentist Tests:

Answer 8

1. Random Sampling:
   * Data must be collected randomly to ensure generalizability
2. Normality:
   * Many parametric tests assume that the data follows a normal distribution
3. Independence:
   * Observations must be independent of each other
4. Homogeneity of Variance:
   * Variance should be similar across groups being compared