The sample analysis was not representative of the ideal population as most of the population data given were biased and had unsubstantiated conclusions that lacked scientific reasoning clarity. The results tend to differ based on the information given by various respondents, making the final sample have an absence of some value, more so on the overall target population. However, if the sample is not representative of the population, it is difficult to generalize results on the overall target population (Spriggs et al., 2014). It is an issue that frequently occurs due to unlimited data, which does not guarantee a sample population. The other view is credited on how parameters might arise if the initial population sample is not representative making a difference in sampling error. The outcome of this sample might be biased since its inclusivity does not abide with relevant data given as to which set of methods are complied.
What are the undesirable consequences of using a poor sampling technique?
Some undesirable consequences in using the sampling technique show that the results might have distortions and be invalid due to errors. Most of these distortions lead users to have incorrect data at its conclusive level. As a data analyst, there is always a need to focus on the entire sampling data to minimize errors and bias, which have a significant effect on data outcome evaluation. The sampling data might have deviations from its initial value if the actual population does not emanate from the initial data obtained. However, the fault of data collection in the sampling session might lead to consequences as the general sampling might be invalid for analysis due to incorrect information which is relayed (Spriggs et al., 2014). Suppose the selection of the sampling data is biased and has errors. In that case, the whole population view might not be effective as this reduce the sample level and make a change in variability as per the estimated data. Lastly, the consequence occurs when the data sample population does not consider all state procedures related to its interpretation, making the outcome information based on the state of estimation.
How could the inaccurate reporting of the data be prevented?
As an analyst, inaccurate reporting of data can be prevented through various ways;
Do not move data on analysis manually, as this is an issue that might induce errors. To prevent this, there is a need to impose an automatic interpretation of data to minimize errors. The other one is making a positive habit of cross-checking the given by consistently making an extra effort which might have various lenses of change.
Be skeptical about the current data on reporting, which minimizes errors and biases, and this is done through the double of the report, which is on analysis and represented. It is an issue that needs keen interest to be imposed and to minimize questionable assumptions that might confirm outcome errors after evaluating the initial data. Furthermore, there is a need to proofread the data report given to avoid final sent-off data that has many errors and needs re-evaluation (Spriggs et al., 2014). It is good to be suspicious when representing statistical information/data as this minimizes errors that occur in different timelines and improves the nature of the consistent level. The nature of data before representation must be monitored and proofread to make the client’s final presentation looks pleasing to the reader in the motive of accuracy improvement.
Reference
Spriggs, A. D., Lane, J. D., & Gast, D. L. (2014). Visual representation of data.
