BCM 328: ADVANCED RESEARCH
METHODS
ALEX NDIRITU
, Inferential statistics
• Inferential statistics is a branch of statistics that involves making inferences or
predictions about a population based on data collected from a sample of that
population. It allows researchers to draw conclusions and make generalizations
about a larger group (population) based on data collected from a smaller subset
(sample)
• Key concepts and techniques in inferential statistics include:
• Hypothesis testing: Hypothesis testing is a fundamental aspect of inferential
statistics. It involves formulating a null hypothesis (H0) and an alternative
hypothesis (H1), collecting data, and using statistical tests to determine whether
there is enough evidence to reject the null hypothesis in favor of the alternative
hypothesis
• Confidence interval: Confidence intervals provide a range of values within which
the true population parameter is likely to lie, with a certain level of confidence. For
example, a 95% confidence interval indicates that if the study were repeated
numerous times, 95% of the intervals would contain the true population parameter
METHODS
ALEX NDIRITU
, Inferential statistics
• Inferential statistics is a branch of statistics that involves making inferences or
predictions about a population based on data collected from a sample of that
population. It allows researchers to draw conclusions and make generalizations
about a larger group (population) based on data collected from a smaller subset
(sample)
• Key concepts and techniques in inferential statistics include:
• Hypothesis testing: Hypothesis testing is a fundamental aspect of inferential
statistics. It involves formulating a null hypothesis (H0) and an alternative
hypothesis (H1), collecting data, and using statistical tests to determine whether
there is enough evidence to reject the null hypothesis in favor of the alternative
hypothesis
• Confidence interval: Confidence intervals provide a range of values within which
the true population parameter is likely to lie, with a certain level of confidence. For
example, a 95% confidence interval indicates that if the study were repeated
numerous times, 95% of the intervals would contain the true population parameter
