Karteikarten
Hesc 349- Hypothesis Testing (one sample)
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- 1HypothesisScientific process with specific steps and terms. No predetermined outcome. we don't know what the outcomes will be.
- 2Null Hypothesis- What we would expect if we analyze the data with statistics, and the results do not confirm our theory.
- The null hypothesis implies “no difference” between the sample and the full population.
H0: µ1 = k
Mean of Variable 1 = Constant
Example: A sample of students in HESC 349 does not have a statistically significantly different average height than all of CSUF students. - 3Alternative Hypothesis (opposite of null hypothesis)What we would expect if we analyze the data with statistics, and the results do confirm our theory.
Usually the alternative hypothesis implies “there IS a difference” between the sample and the full population.
HA: µ1 ≠ k
Mean of Variable 1 ≠ Constant
Example: A sample of students in HESC 349 does have a statistically significant different average height than all of CSUF students. - 4Type 1 errorWhen you reject the null hypothesis, when the null hypothesis is really true.
A “false positive.” A claim that is false.
Example: You tested positive for HIV but really, you’re HIV negative. - 5Type 2 errorWhen you fail to reject the null hypothesis, when the alternative hypothesis is really true.
A “false negative.”
Example: You tested negative for HIV but really, you are HIV positive! - 6Hypothesis Testing- The process in which statistics are used to test a theory.
- You are making inferences about a sample, based upon a theory that presumably applies to whole populations.
- On the other hand, setting confidence intervals is when you make inferences about the population based upon sample data! - 7The steps for Hypothesis Testing***1. State the Null Hypothesis
2. State the Alternative Hypothesis
3. Calculate Test Statistics (z-test or t-test)
4. Make a decision, based upon your error level
-These steps are the same for all hypothesis tests! - 8Z-Test or T-Test?t-distribution = Another type of normal distribution that can be used for hypothesis testing
Use a t-test instead of a z-test when you don’t know the standard deviation of the full population!
If the absolute value of the test statistic is greater than the critical value, then we reject the null hypothesis. - 9Interpreting the Test: Making a DecisionThis is when you decide whether your test statistic is “big enough,” either in the positive or negative direction, to reject the null hypothesis!
The standard cutoff p-value (alpha) is .05.
If p > 0.05, we fail to reject the null hypothesis.
If p < 0.05 (e.g., .04, .03, .0001), then we reject the null hypothesis – yay!
P < 0.05 - 10Describing the Results of the Hypothesis TestIf your statistics support the null hypothesis: You should say that you “fail to reject the null hypothesis.”
The results show no statistically significant difference.
If your statistics support the alternative hypothesis: You should say that you “reject the null hypothesis.”
The results show that there IS a statistically significant difference. - 11"Fail to reject the null hypothesis"If your statistics support the null hypothesis: You should say that you “fail to reject the null hypothesis.”
The results show no statistically significant difference. - 12“Reject the null hypothesis.”If your statistics support the alternative hypothesis: You should say that you “reject the null hypothesis.”
The results show that there IS a statistically significant difference. - 13Calculate the Test StatisticSample statistics
Mean = 67
Standard Deviation = 3.1972
Population = 10
Degrees of freedom = n - 1 = 9
T-statistic = (sample mean) – (population mean) = 1.978
(standard deviation/√n)
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- Karteikarten am Hesc 349- Hypothesis Testing (one sample), erstellt von Noelle Kumasaka am 03/04/2018.
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- Noelle Kumasaka
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