Question
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 :
The correct answer is
(ii), (iv), (i), (iii), (v), (vi)
Understanding the correct sequence of steps is fundamental for conducting a valid statistical significance test.
Statistical Significance Testing Order
The process involves several key stages, executed in a specific order, to determine if research findings are likely due to a real effect or random chance.
Correct Step Sequence
The logical and standard order for testing statistical significance is:
- (ii) State the research question: Clearly define the problem or question being investigated.
- (iv) State the null and alternate hypothesis: Formulate specific, testable hypotheses (H₀ and H₁) based on the research question.
- (i) Select the statistical test: Choose the appropriate statistical method based on the type of data and hypotheses.
- (iii) Compute the test statistic: Calculate the value of the chosen test statistic using the collected sample data.
- (v) Compare the test statistic with the critical value: Determine if the computed test statistic falls into the rejection region defined by the critical value (or compare p-value to alpha).
- (vi) Interpret the results: Make a decision to either reject or fail to reject the null hypothesis based on the comparison, and state the conclusion in the context of the research question.
Following this systematic order ensures accuracy and validity in statistical analysis.
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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 : - 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 : - 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 : - Type-I error in drawing statistical inference while testing the null hypothesis is
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