Clearly State the Null and Alternative Hypotheses 3 Choose the relevant

Clearly state the null and alternative hypotheses 3

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2. Clearly State the Null and Alternative Hypotheses 3. Choose the relevant statistical test 4. Calculate p-value 5. Is p-value<0.05? No Do not reject null Reject null hypothesis Yes 59 60 The process I just described is called hypothesis testing Definition - the formal process to use the statistical properties of the data to evaluate your hypothesis (i.e., “answer” your RQ) Why do we test hypotheses? Current state understanding (conjecture beliefs and prior knowledge) Data (your investigation) A more advanced state of understanding (knowledge) Hypothesis testing Framework of Scientific Investigation Your Null and alt. Hypotheses Outcome of the testing
10/04/2019 21 Some key concepts/terms that are important for you to understand statistical testing conceptually Average (mean) Sample characteristics Population characteristics • Variability 61 Average (mean) Just re-iterating a point made in an earlier slide Why do we care about the mean value: for the vast majority of the variables we work with in this unit, when we refer to them, we refer to their average values • Examples Does overall satisfaction with Crown casino differ between men and women? Who spend more time on the daily commute, Sydney- siders or Melbournians? Statement you regular encounter in the media: such as “Men earn more money than women”; or “Smoking shortens smokers’ lives by about 12 years” 62 Sample characteristics vs. population characteristics Sample characteristics – what you calculate from the sample, e.g., average grade expectation among male respondents is 77.38 Population characteristics The “true” values (e.g., “true” grade expectation among male students) They are what your RQs are really asking! Unfortunately, the values are unknown (and often unknowable) All research questions are about population characteristics , but all data collected are sample characteristics In its essence, the hypothesis testing process is to use observed relationships between sample characteristics to make inference about relationships between the unobservable population characteristics 63
10/04/2019 22 The challenge in using sample characteristics to make inference about population characteristics Sample characteristics population characteristics Sample characteristics vary! 64 Variability Sample characteristics are random variables , e.g., if you collect data for a few times—e.g., with different samples, or at different times—you will get different sample averages This variability is an issue that needs to be accounted for with statistical analysis (in another word, is the difference in satisfaction we observe in the sample between male and female students real, or just noise?) The first step is to quantity the variability in sample characteristics in your data (e.g., through metrics such as variance and standard deviation) Because these are random variables, the outcome of the test involve probabilities (i.e., P value) 65 Quick Recap 66 the challenge we face - the question we ask about population essentially cannot be answered with certainty the solution – we use statistics to infer what the answer probably is (probability)