shanki-big data discussion replies each in 150 words

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Please read below student posts and reply.No need to provide answer to main question.You just need to reply to below posts.

main que:Please provide an example of both a Type I Error and Type II Error. Why is it that increasing the sample size reduces the probability of a Type II error to an acceptable level? Please discuss.

sra-Type I error: otherwise called a “false positive”: the blunder of dismissing an invalid theory when it is in reality obvious. At the end of the day, this is the blunder of tolerating an elective theory (the genuine speculation of intrigue) when the outcomes can be credited to risk. Evidently, it happens when we are watching a contrast when in truth there is none (or all the more explicitly – no factually noteworthy contrast). So the likelihood of making a sort I mistake in a test with dismissal locale R is 0 P R H ( | is valid).

Example: A huge clinical preliminary is done to contrast another medicinal treatment and a standard one. The factual examination shows a measurably noteworthy contrast in life expectancy when utilizing the new treatment contrasted with the bygone one. Be that as it may, the expansion in life expectancy is all things considered three days, with normal increment under 24 hours, and with low quality of life during the time of expanded life. The vast majority would not consider the improvement for all intents and purposes noteworthy.

Type II error: otherwise called a “false negative”: the blunder of not dismissing an invalid theory when the elective speculation is the genuine condition of nature. In other words, this is the mistake of neglecting to acknowledge an elective theory when you try not to have satisfactory power. Doubtlessly, it happens when we are neglecting to watch a distinction when in truth there is one. So the likelihood of making a type II mistake in a test with dismissal locale R is 1 ( | is valid) − P R Ha. The intensity of the test can be ( | is valid) P R Ha

Example:In a t-test for an example mean µ, with invalid theory “”µ = 0” and interchange speculation “µ > 0”, we may discuss the Type II mistake comparative with the general substitute theory “µ > 0”, or may discuss the Type II blunder comparative with the particular exchange speculation “µ > 1”. Note that the particular exchange speculation is a unique instance of the general interchange theory.

Expanding test size makes the speculation test increasingly touchy – bound to dismiss the invalid theory when it is, truth be told, false. In this manner, it expands the intensity of the test. … What’s more, the likelihood of making a Type II mistake gets littler, not greater, as test size increases.Statistical power is emphatically corresponded with the example size, which implies that given the degree of different elements, a bigger example size gives more prominent power. Nonetheless, analysts are likewise looked with the choice to have any kind of effect between factual contrast and logical difference.The populace mean of the conveyance of test means is equivalent to the populace mean of the dispersion being examined from.Thus as the example size expands, the standard deviation of the methods diminishes; and as the example size declines, the standard deviation of the example means increments.
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shi-Example A: In our example, the client had two queries. The first is a query that calls the add function of the list, and calls another query to find the total of each item. Both of these queries return the same data. The second query is just another one of the existing queries and calls the add functions of only two of the elements in the list (Rubin, M. (2019).

The client’s Type I Error shows that the Add function for the first query does not exist, and the list is empty (Rubin, M. (2019).

Type II Errors show that the Add function for the second query exists, but the count of the element in the list is not equal to the total of the results of the first query (Rubin, M. (2019).

Example B: In our example, the list is empty because the Add function for one of the elements was called, and a call of the second element wasn’t returned. When there were two results, the second call of the Add function was not called (Rubin, M. (2019).

This client’s Type I Error shows that the first argument to Add just returns a value, so Type II errors if they occur. They occur because of the fact that in the production of a piece of hardware especially if it is a computer motherboard that is used for computer hardware, the components within the computer are not in order. The resulting error occurs when the component that causes the computer to work in an incorrect state i.e., incorrect is the part of the computer that contains the error. When an error occurs within the computer, this is called an error condition. The computer may not normally make a certain type of change i.e., make a change that results in an error. For example, an error condition might occur when the computer is making data entry. If the computer has a hard drive a component within the computer that contains the data, if a file on the hard drive is corrupted or does not exist, an error condition could occur. If the hard drive or data on the hard drive is missing or corrupted, an error condition might occur (Rubin, M. (2019).

The reason is that in the majority of the population of the world, Type II errors occur for the same reasons as Type I: there is nothing that can improve upon and in any case not replace the inherent defect of random chance inherently random – it is just a process. I have found it very compelling that the probability of a Type II error with the size of a sample size of 100 in any given sample is 2/1, or around one in a million, which is an acceptable size. But there is a crucial difference between the samples in which the Type I error occurs and those in which the Type II errors occur – in the former, there isn’t a lot of variation among the different people in the sample it is an extremely stable distribution, meaning that if you know what the average weight of each person is, you can estimate the population distribution of weight (Rubin, M. (2019).

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