SOLUTION: SJSU Inferential Statistics & Analytics Non Probability Samples Discussion

I’m trying to study for my Computer Science course and I need some help to understand this question.

A:Non-probability samples are cost and time effective. Moreover, they are easy to use and essential when dealing with small population sized (Stephanie, 2020). The challenge lies in determining the reliability of the sample. The reason being, the samples have high probabilities of biases (Stephanie, 2020). The other disadvantage is inability to compute confidence intervals and margins of error. The worst forms are judgmental and snowball sampling strategies. The former involves selection of respondents who seem fit for the study. the researchers rely on their knowledge and experience to recruit participants. They might demonstrate bias in the process. On the other hand, the latter involves selection of the available sample. The sample selected can be friends or relatives to the researcher.Systematic and cluster sampling are significantly different. For instance, cluster sampling allows formulation of clusters (Guest et al., 2017). The clusters are small groups which are classified based on a given criteria. On the other hand, systematic sampling applies fixed intervals to select members of the population (Guest et al., 2017). The inference shows that analyst must know the population size when undertaking systematic sampling.The CLT(Central Limit Theorem) refers to the approximation of a distribution into a normal distribution. It applies to samples larger than 30. Therefore, the parent distribution does not apply given the sample size is large enough to be approximated (Guest et al., 2017). The increasing of sample size increases the possibility of encompassing the characteristics of interest. It also raises confidence in the sample and accuracy in the estimates.————–.(Minimum 160 words) + 1 reference APA formatB:It offers minimal room for judgment, as when an individual is allowed to conduct non-probability sampling, an individual with sound knowledge and skills on the subject will perform best. It is very cost-effective compared with probability sampling and saves time by reducing administrative issues and traveling (Glen, 2020). When the total population is not specified, it suits best.It is excessively dependent on since this method does not have any rules for sample selection, leading to biases and prejudice on the subject matter. Besides, this method requires purpose-oriented pollsters as it relies on the judgment. Moreover, it focused on simplicity rather than its effectiveness (Glen, 2020).The worst form of non-probability samples is convenience sample often as it is completely based on bias. In this method, the sample is not chosen randomly to represent the population that is being studied. So this limits the ability to make generalizations from our sample to the population to be analyzed.The primary difference between the two sampling techniques is that in cluster sampling, the clusters created are heterogeneous, whereas, in stratified sampling, the groups are homogeneous(Glen, 2019). When the entire population is known, systematic sampling works better, while cluster sampling works best when it is hard to quantify the whole population. Another distinction is that patterns must be avoided for precision in stratified sampling, and clusters should, on the other hand, have identical characteristics to the entire sample.The Central Limit theorem means that sample distribution approximates a normal distribution as the sample size increases, thus assuming that all samples are equal in size, irrespective of the form of the population distribution (GANTI, 2019). A main feature of CLT is that the average of the means and standard deviations of the sample would be equal to the mean and standard deviation of the population. Sufficiently large sample size will reliably predict a population’s features.A general rule of thumb is that sample sizes equal to or greater than 30 are considered adequate to maintain the CLT, indicating that the distribution of the means of the sample is generally equally distributed (GANTI, 2019). Therefore, as our sample data size increases, our estimation confidence increases, and hence we will have greater precision and less uncertainty.————–.(Minimum 160 words) + 1 reference APA formatNote:Must be entirely focused on the specific content.Add some generic points that could applyPlease check plagiarism, GrammarlyAPA FormatRecommended Textbooks:Discovering Statistics, by Hawkes and Marsh. Published by Hawkes Learning Systems. Chapters 9.Lind, Marchal, Wathen, Statistical Techniques in Business and Economics, 16th Edition. Chapters 8.

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