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What are the common issues with Statistics? Some of the issues of statistics are highlighted below; Ambiguous percentages are sometimes used Using small samples Manipulation of scale and axis in graphs and charts Poor or unreliable statistical surveys Giving meaning to minor differences in parameters Seeking solution for Statistics Homework Help?

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So, if you have some problems with statistics or want to take a pressure off, do not hesitate to contact us anytime you want. Furthermore, other professions such as medicine, armed forces, engineering, etc. For instance, doctors can analyze the medical history of patients before suggesting him a treatment in order to prevent the disease. In addition, armed forces also use statistical methods for the purpose to make strategies.

Strategies require extensive scientific knowledge and assumptions which cannot be achieved without taking the help from the area of statistics. In the field of statistics, a lot of ways through which one collects the data. The purpose of data collection is to make future predictions or derive solutions for the present problems.

One of the major tasks in the field of statistics is to collect the data; however the analysis of findings and conclusion comes later. Data collection tools include questionnaire, focus group, in-depth interviews and many others. It is necessary that the data collection tool should be designed or selected as per the type of research or the attributes of the population.

All the researches are mainly based upon the data collection methods; however one is unable to conduct a research without collecting the data. In statistics, there are numerous data collections methods are used for conducting a research in which some of them are experimental studies and observational studies, sampling, etc.

In the causal studies, the data collection method such as experimental as well as observational are used in order to gather the data from population.

Causal study is the type of quantitative research. In this kind of study, researcher finds the cause and effect among two variables. Researcher constructs a hypothetical condition which called hypothesis between the variables so that cause and effect will be determined.

Work performance and stress is one of the best examples of the causal studies. Moreover, the researcher mostly collects the data through observation or experimental methods in the causal studies. In the observational method, researchers can closely observe the activities of respondents for the purpose to collect the data. For instance, use of mystery shoppers is one of the best examples of data collection through observations. On the other hand, researcher can design an experiment for the respondents in order to do data collection.

The other kind of data collection method is sampling. It is also used as the method of data collection through which researcher gathers the responses from the participants about a particular issue or problem.

Sampling data collection method is used to collect responses from the large population. Sampling is the most preferable data collection methods for the qualitative studies.

In sampling, it is convenience to collect large amount of data in a few time. Furthermore, sampling data collection method has two kinds that include probability sampling and non-probability sampling. Probability sampling data collection method is also known as the random sampling. In this kind of data sampling technique, researcher is free to choose the elements from the sample on a random basis. It means that all the elements of the sample have an equal chance to select by the researcher.

On the other side, non-probability sampling is also the data collection method. In this type of sampling, researchers have the authority to choose or select only those elements that are convenience to select. In the field of statistics, it is necessary for the researches to first identify the type of data before collecting the data. The reason is that if the data collection tool is not equivalent with the data type, then it will be interpreted in a wrongful manner.

Statistics has different kinds of data categories which should be identified in order to do correct data analysis. Statistics is the field of study or an academic discipline which mainly comprises on data. The fundamental function of statistics is to gather data for the purpose to draw conclusions or solve complex issues with the help of logical reasoning.

It is better to categorize the data first before starting the data collection. In addition, categorization of data also helps the researcher in order to best data collection method. Particularly, there are seven main fundamental kinds of data which is used in the field of statistics. The seven types of data are ordinal, categorical, binary, real valued multiplicative, real valued additive, count and binomial.

Each data type has its specific characteristics that should be incorporate with the data collection methods. The description of all data types are given below:. Ordinal is the data type in the field of statistics. In this type of data, the data represents in the form of real numbers as well as integers till the level of arbitrary.

Ordinal scale is the level of measurement which is used to measure ordinal data. For instance, the salary of a person is considered as the example of ordinal data. Categorical is also the type of data in statistics. Categorical data type is quite similar to the binary data type that is because the permissible statistics as well as the measurement scale of both these data types are same.

The categorical data can be written in the form of number that starts from 1 and ends at infinity value. Its values are based on random choices or the judgment of an individual. The example of categorical data type is the selection of a political party by an individual in order to become a member. The third type of data called binary.

Binary data type represents in only two digits that include 0 and 1. Computers only understand the language of 0 and 1. The numbers 0 and 1 function as yes or no. In addition, this data type is only used where the possible outcomes are only two. In statistics, nominal scale is used as the measurement scale binary data.

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