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A large sample reduces the variability of the estimate. The extent to which variability is reduced depends on the quality of the sample, what variable is being estimated and the underlying distribution for that variable.
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How does sample size affect the validity of an experiment?
The sample size has no effect on the validity of an experiment: instead, it is the experimental procedure and integrity of the experimenters.The sample size can affect conclusions that may be drawn from an experiment. The larger the sample is, the more reliable these conclusions are.
What kind of validity does the strategy of random sampling increase?
Random Sampling increases the reliability and validity of your research findings. To begin with, Reliability: By randomly picking research participants, the likelihood that they are from different backgrounds/ have different experiences etc. is higher and hence, they are said to be more representative of the population of interest. EG: RQ: Do females have higher IQ? A case of random sampling will pick females who are housewives/ CEOs/ Indian/ 18yrs old/ Divorced etc. the list goes on. While a case of non-random sampling (such as picking participants at a bus stop) may only result in a sample of females who are 20 - 35 years old, working professionals. Validity: As reliability and validity are related, for the research findings to be reliable and generalizable to the population of interest, it first has to be a valid sample. Hence, from the above example, EG1 provides a valid sample, while EG2 is invalid.
What is he conclusion from research conducted on a sample population to the population as a whole?
Assuming that the population was carefully defined, the sample population was carefully and correctly chosen, and that there were significant results, then the implication is that the results of the study, within the confidence limits indicated, hold true for the population at large.
Which of these best describes why it is so important to have a representative sample?
To generalize results from the sample population to the target population.
How does sample size effect the test statistic?
The larger the sample size, the more accurate the test results.
How does sample size affect the validity of an experiment?
The sample size has no effect on the validity of an experiment: instead, it is the experimental procedure and integrity of the experimenters.The sample size can affect conclusions that may be drawn from an experiment. The larger the sample is, the more reliable these conclusions are.
What is the differences between external and internal validity in research?
Internal validity has to do with the accuracy of the results. Results could be inaccurate if samples are not selected randomly. External validity has to do with the generalizability of the findings to the population. If the sample selected is only Hispanics under the age of 25, then it would be hard to generalize the results to the entire US population.
How does sample size affect the experiment?
The sample size determines the accuracy of results in an experiment
How will you ensure validity and reliability of data?
To ensure validity and reliability of data, market researchers can present the same surveys to different respondents within the same sample area. Using a wider sample area will also give a true picture of the results.
What can affect the results of saliva sample testing?
Smoking, eating, drinking, and brushing the teeth can affect test results, as can the way in which the person puts saliva on the slide.
How might choosing a small sample size affect a scientific investigation?
Statistically the results will not be scientifically valid if the sample size is too small.
What is the relationship between the problem statement and the research design?
Sample design and research design are two closely related concepts in research methodology, and the two are often interdependent. Research design refers to the overall plan or strategy for conducting research, including the selection of research methods, data collection procedures, and data analysis techniques. The research design is typically determined by the research question and the purpose of the study. Sample design, on the other hand, refers to the process of selecting a sample from a larger population for research or data analysis. The sample is a subset of the population that is selected to represent the population's characteristics accurately. The sample design is determined by the research question, the research design, and the population's characteristics. The relationship between sample design and research design is that the sample design is a critical component of the research design. The research design determines the overall approach to the study, while the sample design determines the specific subset of the population that will be studied. The research design guides the selection of research methods, data collection procedures, and data analysis techniques, while the sample design determines the size of the sample, the sampling method, and the criteria for inclusion in the sample. The sample design must be aligned with the research design to ensure that the sample represents the population's characteristics accurately and that the results are valid and reliable. Therefore, sample design and research design are interdependent and must be carefully considered when conducting research to ensure that the results are meaningful and accurate.
Why would it be important to consider selection attrition and history?
Considering selection, attrition, and history is important because they can impact the validity and generalizability of research findings. Selection bias can affect the representativeness of the sample, attrition can lead to missing data and potential bias, and history can confound the results by external events occurring during the study period. By addressing these factors, researchers can improve the rigor and reliability of their findings.
What do you mean by sample design?
Sample design refers to the process of selecting a sample from a larger population for research or data analysis. The sample is a subset of the population, which is selected to represent the population's characteristics accurately. Sample design involves determining the size of the sample, the sampling method, and the criteria for inclusion in the sample. The size of the sample is typically determined based on the desired level of precision, level of confidence, and resources available for the research or data analysis. The sampling method can be random, stratified, cluster, or systematic, depending on the research question and the characteristics of the population. The criteria for inclusion in the sample are determined by the research question and the population's characteristics. For example, if the research question is about the prevalence of a particular disease in a population, the sample design may include criteria for age, gender, and other demographic variables to ensure that the sample represents the population's characteristics accurately. Sample design is a critical aspect of research and data analysis, as it directly affects the accuracy and generalizability of the results. A well-designed sample can help to minimize bias and increase the reliability of the results, while a poorly designed sample can lead to inaccurate or misleading conclusions. Therefore, it is essential to carefully consider sample design when conducting research or data analysis to ensure that the results are valid and reliable.
What are the weaknesses of infrared spectroscopy?
Infrared spectroscopy cannot be used quantitatively. The sample preparation is also complex. It may be robust as the sample preparation may affect its results.
What kind of validity does the strategy of random sampling increase?
Random Sampling increases the reliability and validity of your research findings. To begin with, Reliability: By randomly picking research participants, the likelihood that they are from different backgrounds/ have different experiences etc. is higher and hence, they are said to be more representative of the population of interest. EG: RQ: Do females have higher IQ? A case of random sampling will pick females who are housewives/ CEOs/ Indian/ 18yrs old/ Divorced etc. the list goes on. While a case of non-random sampling (such as picking participants at a bus stop) may only result in a sample of females who are 20 - 35 years old, working professionals. Validity: As reliability and validity are related, for the research findings to be reliable and generalizable to the population of interest, it first has to be a valid sample. Hence, from the above example, EG1 provides a valid sample, while EG2 is invalid.
What is the meaning of External Validity?
External validity is a term used to describe the extent that results obtained from one study can be generalised to other situations. For instance, a study only using a very small sample of participants from one particular city might not relate to other cities or people.
