ASSIGNMENT discrete units and are applied to mark

Course name: Course number: Assignment title or task:
(You can write a question) Distinguish between different types of data
What’s the difference between a population and a sample in statistics?
What is the purpose of hypothesis testing
How to interpret confidence levels and confidence intervals?
Null hypothesis
Alternative hypothesis
Type I error
Type II error
Why p-value is important?
Student name: Student ID: Submission date: To be filled in by the instructor only
Instructor name: Grade:
Data types are a crucial theory because statistical systems can only be implemented for specific data types (QUADER, A. 2018). To begin with, Ordinal data delineate discrete and distributed units. It is consequently nearly the same as nominal data, except that its organization matters. Secondly, Nominal data describe discrete units and are applied to mark variables that have no quantitative state. Thirdly, Categorical data depicts characteristics. Hence it can describe things like a person’s gender and language. Finally, Continuous Data describes measurements and hence their values can’t be calculated but they can be measured.

Population indicates a broad collection consisting of elements containing at least one common feature (QUADER, A. 2018). The term is usually differentiated with the sample, which is nothing but a part of the population that is so elected to represent the entire group. Population represents the entirety of characters, bits, pieces and anything that is capable of being observed, having unique properties. On the opposite, the sample is a measurable subset of the population that is determined by a systematic process, to discover the features of the original set.
The fundamental purpose of hypothesis testing is to pick between two competing hypotheses about the state of a population parameter (QUADER, A. 2018). Hypothesis testing is an imperative method in statistics. A hypothesis test assesses two mutually independent statements about a population to resolve which statement is best bolstered by the sample data.

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95% of the time, when we interpret a confidence interval, the actual meaning will be within the two values. 5% of the time, it will not. Since the true population means is an undiscovered value, we don’t understand if we are in the 5% or the 95%. BUT 95% is moderately good so we assume something like. It’s important to note that we can’t have a 100% confidence interval. By description, the population mean is not identified. It’s not plausible to calculate these levels and intervals since that would mean that we need an accurate census of a population and this is not workable.

A null hypothesis is a variety of hypothesis employed in statistics that implies that no statistical importance exists in a set of given observations (QUADER, A. 2018). The null hypothesis is a general description or default state that there is no relationship between two estimated phenomena or no connection among groups. Additionally, in statistical hypothesis testing, a type I error is the dissolution of a true null hypothesis, while a type II error is slipping to decline a false null hypothesis.

The p-value is the level of external importance within a statistical hypothesis test representing the likelihood of the occurrence of a given situation. The p-value is important and it’s used as an option to rejection points to present the least level of weight at which the void hypothesis would be spurned.


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