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Ch2W4L1.pptx

# Ch2W4L1.pptx - CHAPTE 2 R HYPOTHESIS TESTING INTRODUCTIO N...

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CHAPTE R 2 HYPOTHESIS TESTING

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INTRODUCTIO N Why we should employ the method of hypotheses testing? ü sampling variability. ü to make a statement (or claims) regarding the value of population parameter based on sample information.
Statistical hypothesis testing is a decision-making process for evalua t ing clai m s about a population. In hypothesis testing, the researcher must: i. define the population under study, ii. state the particular hypotheses that will be investigated, iii. give the significance level, iv. select a sample from the population, v. collect the data, vi. perform the calculations required for the statistical test, vii. and reach a conclusion

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Hypotheses concerning parameters such as mean(s) and proportion(s) can be investigated. The z tes t and the t -tes t are used for hypothesis testing concerning mean(s).
Methods of Hypotheses testing Step 1 State the hypothesis, and identify the claim. Step 5 Summarize the results/ Conclusion Step 2 Compute the test value. Traditional method Step 4 Make decision whether to reject or not reject H 0 Step 3 Find the critical value from the appropriate able. Figure 2.1: Flow chart for traditional method of hypothesis testing

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Step 1 State the hypothesis, and identify the claim. Step 5 Summarize the results/ Conclusion Step 2 state the p-value from SPSS output P-value based method Step 4 Make decision whether to reject or not reject H 0 Step 3 State the significance level, α Figure 2.2: Flow chart for p-value based method of hypothesis testing
HYPOTHESIS-TESTING FOR MEAN AND PROPORTION Step 1 : S t a t ement of a H y po t hes i s A statistical hypothesis is a conjecture about a population parameter which may or may not be true. There are two types of statistical hypotheses for each situation: The null hypothesis : symbolized by ü H 0, no di f fe r en c e bet w een a pa r ameter and a s pe c ific valu e , or that there is no difference between two parameters.

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ü The alternative hypothesis: symbolized by H 1 , § exi s ten c e of a di f fe r en c e bet w een a pa r ameter and a s pe c ific value N ull h y po t hesis is the status quo, can be expressed in one of these three forms: ü Testing population mean H 0 : µ H 0 : µ H 0 : µ = k ≤ k ≥ k ü Testing population p r opo r tion H 0 : H 0 : H 0 : p p p = k ≤ k ≥ k
N u l l hyp o t hesis must be accepted if the alternative hypothesis is not accepted (failed to reject) as a result of the hypothesis testing N u l l hy p o t he s is is always about a pop u l a t ion pa r a m e t e r , not about a sa m ple s t a t is t ic

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p A l t e rn a t i v e and null h y po t hes i s can be express in one of these 3 forms: Table 2.1: Alternative andnull hypothesis One-tailed, lower/ left tail H 0 : µ ≥ k Vs H 1 : µ < k One-tailed, upper/ right tail H 0 : µ ≤ k Vs H 1 : µ > k Two-tailed H 0 : µ = k Vs H 1 : ≠ k One-tailed, lower/ left tail H 0 : p k Vs H 1 : < k One-tailed, upper/ right tail H 0 : p k Vs H 1 : > k Two-tailed H 0 : p = k Vs H 1 : ≠ k Testing proportion Testing mean
.5-a \ a \ 0 a. Form H of 1 H :

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• Fall '13
• Statistics, Null hypothesis, Statistical hypothesis testing, researcher

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