Inferential statistics helps researchers make predictions, test hypotheses, and generalize findings from a sample to a larger population. It turns raw data into valuable insights and guides clinical decisions.
In biomedical research, gathering data is only the first step. Making reliable inferences from that data is the true challenge and the source of value. In biostatistics, where choices frequently impact clinical treatments, public health initiatives, and healthcare policy, inferential statistics become crucial in this situation.Biostat Prime gives us the flexibility to perform these tests easily.
Since we rarely have full population data, we study samples and use statistical methods to infer conclusions about the whole population.
While descriptive statistics summarize data, inferential statistics help with predictions and decision-making.
Helps determine if an observed effect is real or due to chance. Common tests include:
A range of values likely to include the true value. Example: "The mean blood pressure reduction was 8 mmHg, with a 95% CI of 5 to 11 mmHg."
Used to understand and predict relationships:
A p-value shows the likelihood of seeing your results by chance. p < 0.05 is usually considered significant.