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– Hypothesis Testing: A statistical procedure used to identify hypotheses from a given set of data and make decisions about them based on the evidence.
– Null Hypothesis: The statement that there is no relationship between two variables being studied. This serves as a baseline against which to compare any findings from the study.
– Alternative Hypothesis: A hypothesis that states that there is some form of relationship between two variables being studied, which can be either positive or negative in nature.
– Type I Error: Also known as a false positive, this occurs when the null hypothesis is incorrectly rejected (when it should not have been).
– Type II Error: Also known as a false negative, this occurs when the null hypothesis is incorrectly accepted (when it should have been rejected).
– p Value: The probability of obtaining results at least as extreme as those observed in the sample if the null hypothesis were true. If this probability is low (less than 0.05), then we can reject the null hypothesis and accept the alternative hypothesis instead.
Principles of Hypothesis Testing:
1) Formulate hypotheses – Identify variables you want to test, state your assumptions about them, and create both your null and alternative hypotheses accordingly.
2) Collect data – Determine an appropriate sample size and design an experiment or survey to collect relevant data points on each variable under investigation.
3) Analyze data – Using statistical techniques such as t-tests or chi square tests to determine how likely it is that these observations could occur randomly under our assumption stated by our null hypothesis (this will give us our p value).
4) Make conclusion – Compare your p value with predetermined standard values like 0.05 and decide whether to reject or fail to reject your alternatives depending on their magnitude compared with this standard value.