# MCQs | Data Analytics – Sampling

Data Analytics – Sampling MCQs: This section contains the Multiple-Choice Questions & Answers on Data Analytics – Sampling with explanations.
Submitted by IncludeHelp, on February 04, 2022

1. Statistical data analysis deals with the usage of statistical tools.

1. True
2. False

Explanation:

Statistical data analysis is concerned with the application of specific statistical tools, which necessitates the knowledge of statistics. Statistical data analysis is a procedure that entails the application of a variety of statistical operations. In quantitative research, it is a type of research that seeks to quantify the data by using some form of statistical analysis, which is typically applied. Survey data and observational data are examples of quantitative data, which is primarily comprised of descriptive data.

2. Sampling is a process used in ___.

1. Network setting
2. Statistical analysis
3. Semantic analysis
4. None of the mentioned above

Explanation:

Sampling is a statistical analysis technique in which a predetermined number of observations are drawn from a larger population in order to conduct statistical analysis. Simple random sampling or systematic sampling may be used to select samples from a larger population, depending on the type of analysis being performed.

3. Probability sampling is a sampling technique where we set a selection of a few criteria and chooses members of a ___ randomly.

1. Population
2. Employee
3. Both A and B
4. None of the mentioned above

Explanation:

Probability sampling is a sampling technique in which a researcher selects a small number of criteria and then randomly selects members of a population from that selection. With this selection parameter, all of the members have an equal chance of becoming a part of the sample population.

4. Cluster sampling is a method where we divide the entire population into ___.

1. Sections
2. Area
3. Both A and B
4. None of the mentioned above

Explanation:

Cluster sampling is a technique in which researchers divide a large population into sections or clusters that are representative of a population under investigation. Clusters are identified and included in a sample based on demographic parameters such as age, gender, and geographic location, among others. Because of this, it is very simple for the creator of a survey to derive useful inferences from the responses received.

5. ___ to choose the sample members of a population at regular intervals.

1. Systematic sampling
2. Random sampling
3. Stratified random sampling
4. None of the mentioned above

Explanation:

Using the systematic sampling method, we can randomly select members of a population to represent a sample on a regular basis. Starting points for the sample as well as a sample size that can be repeated at regular intervals are necessary for this procedure to be successful. Due to the fact that this type of sampling method has a predetermined range, it is the least time-consuming of the sampling techniques.

6. ___ divides the population into smaller groups that don't overlap but represent the entire population.

1. Systematic sampling
2. Stratified random sampling
3. Probability sampling
4. None of the mentioned above

Explanation:

Stratified random sampling is a technique that divides a population into smaller groups that do not overlap but are representative of the entire population. While sampling, these groups can be organized and then a sample drawn from each group can be used to determine the results.

7. Amongst which of the following is / are the uses of probability sampling -

1. Reduce Sample Bias
2. Diverse Population
3. Create an Accurate Sample
4. All of the mentioned above

Answer: D) All of the mentioned above

Explanation:

Probability sampling can be used in a variety of situations. The bias in a sample derived from a population is negligible to non-existent when the probability sampling method is used, according to the literature. The researcher's understanding and inferences are primarily reflected in the sample he or she chooses for analysis. Population with a Wide Range of Characteristics - When the population is large and diverse, it is critical to have adequate representation so that the data is not skewed towards one demographic. Additionally, it generates a precise Sample. Probability sampling aids in the planning of the research and the creation of an accurate sample. This aids in the collection of well-defined data.

8. Judgmental or purposive sampling is formed by the purpose of the study, along with the understanding of the target audience.

1. True
2. False

Explanation:

Alternatively referred to as purposive sampling, this nonprobability sampling technique selects participants for a sample based solely on the knowledge and judgement of the researcher. The use of judgement sampling increases the relevance of the sample to the population of interest because only those individuals who meet specific criteria are selected for inclusion in the sample. Researchers strive to use the sample that is the most representative of the population of interest in order for a study to be carried out as efficiently and effectively as possible.

9. ___ sampling takes a consecutive series of items.

1. Block
2. Set
3. Heuristic
4. None of the mentioned above

Explanation:

Block sampling is a method of selecting a sample from a population by selecting a series of items from the population in a sequential manner. Block sampling is a sampling technique used in auditing that involves making a series of selections in a sequential fashion. This method is extremely efficient because it allows for the retrieval of a large number of documents from a single location. While a more random selection method would be preferable for sampling the entire population, it would be more effective in this case. A large number of sample blocks can be selected from a pool of samples when using block sampling to reduce the likelihood of sampling error.

10. If the data has a singular variable, then univariate statistical data analysis can be conducted -

1. True
2. False

Explanation:

The data consists of variables that are either univariate or multivariate, and depending on the number of variables, the experts use a variety of statistical techniques to analyze the data. Once a single variable has been identified in the data, univariate statistical data analysis can be performed, such as the t-test for significance, the z test, the f test, the ANOVA one-way test, and so on. And, if the data contains a large number of variables, various multivariate techniques, such as statistical data analysis, discriminant statistical data analysis, and so on, can be used.

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