UGC NET Commerce Business Statistics and Research Methods Flashcards

24 sample cards from the Business Statistics and Research Methods deck

Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.

1. What is classification of data and what are its four main bases?

   Classification is arranging data into homogeneous groups based on common characteristics. The four bases are: Geographical (area-wise), Chronological (time-wise), Qualitative (attributes), and Quantitative (numerical/class intervals).

2. Differentiate between a population (universe) and a sample.

   A population is the entire group of items/individuals about which conclusions are drawn. A sample is a representative subset selected from the population that is actually studied to make inferences about the whole population.

3. Distinguish between a parameter and a statistic.

   A parameter is a numerical characteristic of a population (e.g., μ, σ, population proportion P). A statistic is a numerical characteristic computed from sample data (e.g., x̄, s, sample proportion p) used to estimate the parameter.

4. What is the difference between probability and non-probability sampling?

   In probability (random) sampling every unit has a known, non-zero chance of selection, allowing measurement of sampling error. In non-probability sampling selection is based on judgement/convenience, so probability of selection is unknown.

5. List the main methods of probability sampling.

   Simple random sampling, Systematic sampling, Stratified random sampling, Cluster sampling, and Multistage sampling. All allow every unit a known chance of selection and permit estimation of sampling error.

6. List the main methods of non-probability sampling.

   Convenience sampling, Judgement (purposive) sampling, Quota sampling, and Snowball sampling. Selection relies on the researcher's discretion rather than random chance.

7. Distinguish between stratified sampling and cluster sampling.

   In stratified sampling the population is divided into homogeneous strata and units are drawn from every stratum (maximises within-group similarity). In cluster sampling the population is split into heterogeneous clusters and entire selected clusters are surveyed.

8. What is a sampling distribution?

   A sampling distribution is the probability distribution of a statistic (e.g., the sample mean) obtained from all possible samples of a given size drawn from a population. It forms the basis of statistical inference.

9. State the Central Limit Theorem (CLT).

   The CLT states that as sample size increases (generally n ≥ 30), the sampling distribution of the sample mean approaches a normal distribution with mean μ and standard deviation σ/√n, regardless of the population's shape.

10. Define standard error and give the formula for the standard error of the mean.

    Standard error is the standard deviation of the sampling distribution of a statistic; it measures sampling variability. Standard error of the mean = σ/√n. A smaller standard error means a more precise estimate.

11. Differentiate between point estimation and interval estimation.

    Point estimation gives a single value as an estimate of a parameter (e.g., x̄ for μ). Interval estimation provides a range (confidence interval) within which the parameter is expected to lie with a stated confidence level (e.g., 95%).

12. What are the desirable properties of a good estimator?

    Unbiasedness (expected value equals the parameter), Consistency (converges to the parameter as n increases), Efficiency (smallest variance among unbiased estimators), and Sufficiency (uses all information in the sample).

13. What is the difference between Type I and Type II errors?

    A Type I error (α) is rejecting a true null hypothesis (false positive). A Type II error (β) is accepting (failing to reject) a false null hypothesis (false negative). The significance level controls α.

14. When is a z-test used in hypothesis testing?

    A z-test is used for large samples (n ≥ 30) or when the population standard deviation is known, with data assumed normally distributed. It tests means/proportions using the standard normal distribution; e.g., z = (x̄ − μ)/(σ/√n).

15. When is a t-test used and how does it differ from a z-test?

    A t-test is used for small samples (n < 30) when the population standard deviation is unknown (estimated by s). It uses the t-distribution with (n−1) degrees of freedom, which is flatter than the normal curve and approaches it as n grows.

16. What is ANOVA and what hypothesis does it test?

    Analysis of Variance (ANOVA) tests whether the means of three or more groups are equal by comparing between-group variance with within-group variance using the F-ratio (F = MS between / MS within). H₀: all population means are equal.

17. Differentiate between one-way and two-way ANOVA.

    One-way ANOVA examines the effect of a single factor (independent variable) on a response. Two-way ANOVA examines the effects of two factors simultaneously and can also test their interaction effect on the dependent variable.

18. What is the chi-square test and its main applications?

    The chi-square (χ²) test is a non-parametric test based on χ² = Σ(O − E)²/E (observed vs expected frequencies). It is used as a test of goodness of fit, a test of independence of attributes, and a test of homogeneity.

19. What is the Mann-Whitney U-test and what is its parametric equivalent?

    The Mann-Whitney U-test is a non-parametric test comparing two independent samples by ranking all observations to test whether they come from the same distribution. It is the non-parametric alternative to the independent-samples t-test.

20. What is the Kruskal-Wallis H-test and when is it used?

    The Kruskal-Wallis H-test is a non-parametric, rank-based test used to compare three or more independent samples. It is the non-parametric alternative to one-way ANOVA, applied when data are ordinal or normality cannot be assumed.

21. State Spearman's rank correlation coefficient formula and its range.

    Spearman's rank correlation: rs = 1 − [6ΣD²/(n³ − n)], where D is the difference between paired ranks and n is the number of pairs. Like r, it ranges from −1 to +1 and is used for ordinal/ranked data.

22. Describe the typical structure of a research report.

    A research report has three parts: (1) Preliminary section (title page, acknowledgement, table of contents, abstract); (2) Main body (introduction, review of literature, methodology, analysis/findings, conclusions and recommendations); (3) End matter (references/bibliography, appendices).

23. List the key elements found in the main body of a research report.

    Introduction (problem, objectives, hypotheses), Review of literature, Research methodology (design, sampling, data collection, tools), Data analysis and interpretation/findings, and Conclusions, suggestions and limitations.

24. State common formatting guidelines for a research report.

    Use a standard citation style (APA/MLA/Chicago) consistently; maintain uniform font (e.g., Times New Roman 12pt), double or 1.5 line spacing, and 1-inch margins; number pages; label all tables and figures; and ensure references match in-text citations.

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