Hypothesis Testing: Key Concepts
Hypothesis testing is a critical component of A Level binomial hypothesis testing. It involves making inferences about a population based on sample data.
Important terms in hypothesis testing:
- Population: The entire group about which information is sought.
- Sampling unit: An individual member of the population that can be sampled.
- Sampling frame: The collection of all sampling units.
- Target population: The group from which the sample may be taken.
- Sampling bias: Occurs when the sample doesn't represent the population accurately.
Definition: A null hypothesis H0 is the expected or theoretical outcome, while the alternative hypothesis H1 is what you are attempting to prove.
Types of alternative hypotheses:
• One-tailed: Specifies whether the parameter is greater than or less than the value in H₀
• Two-tailed: Does not specify the parameter, only states that it differs from H₀
Highlight: In hypothesis testing, you always reject the hypothesis that isn't true, rather than accepting the alternative.
Key statistical concepts:
• P-value: The probability for your population, calculated from your sample, assuming the null hypothesis is true.
• Significance level: The probability of rejecting H₀ when it is true, commonly set at 1%, 5%, or 10%.
Vocabulary: The binomial distribution is denoted as Bn,p, where n is the number of trials and p is the probability of success.