Question
Arrange in order the steps in hypothesis testing about a single population mean using the normal curve model :
(a) Decide of n and draw a random sample
(b) Reject/retain Ho on basis of z calculated
(c) Calculate $\bar{\text{x}}$ and $\sigma_{\bar{\text{x}}}$
(d) Identify $\alpha$ level
(e) Define the target population
(f) Specify Ho and HA
(g) Calculate z-score
Code :
The correct answer is
(e), (f), (d), (a), (c), (g), (b)
To correctly order the steps in hypothesis testing about a single population mean using the normal curve model, follow the standard statistical procedure:
Ordering Hypothesis Testing Steps
The logical sequence for hypothesis testing is as follows:
- Define the target population: Clearly identify the group you are studying. (e)
- Specify Ho and HA: Formulate the null hypothesis (Ho) and the alternative hypothesis (HA). (f)
- Identify α level: Choose the significance level (α), which represents the probability of a Type I error. (d)
- Decide sample size (n) and draw sample: Determine the sample size needed and collect a random sample from the population. (a)
- Calculate sample statistics: Compute the sample mean ($\bar{\text{x}}$) and the standard error of the mean ($\sigma_{\bar{\text{x}}}$). (c)
- Calculate z-score: Determine the test statistic, the z-score, using the sample data and hypothesized population mean. (g)
- Reject/retain Ho: Make a decision based on the calculated z-score relative to the critical value(s) or p-value. (b)
Therefore, the correct order is (e), (f), (d), (a), (c), (g), (b).
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Important Questions from Hypothesis testing
- Assertion (A) : When sample size is small and/or data are badly skewed (so that assumption of normality is doubtful) parametric methods are not applicable.
Reason (R) : In parametric tests we can assume that scores upon which our statistics are based are normally distributed in the population.
Code : - Arrange in order the steps in testing for statistical significance :
(i) Select the statistical test to be applied
(ii) State the research question
(iii) Compute the test statistic
(iv) State the null and alternate hypothesis
(v) Compare the test statistic with the critical value
(vi) Interpret the results.
Code : - When Ho is rejected and infact it is true, the researcher is committing a :
- Assertion (A) : Assumption free tests are some what less powerful than parametric techniques when assumption for the latter are met.
Reason (R) : Assumption free tests are less powerful because the researchers do not use scientific sampling methods.
Code : - Type-I error in drawing statistical inference while testing the null hypothesis is
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