Probability Sampling: Random Selection & Joint Probability - Essay Sample

Introduction

The probability sampling procedure is a sampling method that utilizes random selection forms. The only way one will achieve a random selection method is by setting up systems and processes that assure the different units of the population and the alignment of the probabilities chosen (Etikan, 2017). Many people practice the forms of random selection through choices, picking, and naming. When working with research, the best method to use is the joint probability.

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The notion of probability to something such as time and point portrays different definitions that are useful nonetheless. The best method to use is the joint probability, particularly in the perspective of education (Etikan, 2017). This method has advantaged that are conceptually simple in many situations. Various techniques, such as random variables, can also be used to quantify the joint probability.

The techniques offer the bases of probabilistic understanding in the fitting of the predictive model of data. Joint probability makes things happen together. The difference with conditional probability is that there is one probability of one thing taking place (Etikan, 2017). Quantitatively, the difference is that in joint probability, the researcher can evaluate across all combinations because the expectation is divided by the total sum.

The Four Types of Data Collection and the One to use to establish validity and reliability

The four scales of measurements include ratio, interval, ordinal, and data (Goneppanavar et al., 2019). In data collection, the secondary data collection would be ideal for establishing validity and reliability because secondary information uses existing data sources that are readily available and cheaper while setting research purposes.

A few researchers argue that the years of experience in the source in the database are impossible to collect through prospective studies (Goneppanavar et al., 2019). However, the truth of the matter is that the primary data assessment can only be validated through secondary information. It’s only through the secondary data that data mining can create theoretical knowledge in research.

Types of Research Design and Collection Strategy

Data is typically gathered through quantitative and qualitative methods. In quality, approaches address the ‘why’ and ‘how’ of a program that uses unstructured data methods to explore the topic (Schlickel, 2012). Both qualitative and quantitative data must be collected to collect the answers to the research questions. Through educational means or work, the qualitative and quantitative data collection procedures will only be done through interviews, observations, progress tracking, focus groups, questionnaires, and surveys. For quantitative data collection methods, it is essential to understand that the peer-based programs are challenging to implement due to the lack of necessary sources to ensure that the implementation of surveys due to the experienced factors (Schlickel, 2012).

Experiences with Hypothesis Testing

A hypothesis is a procedure of making educated or references guesses on a specific parameter. The process can be done using sample data and statistics or through an uncontrolled observational study. The statistical inference will be developed through these modules to address the hypothesis testing (Bashir, 2018). The specific hypothesis and statement are generated through sample statistics and population parameters to ensure that the theory is correct.

The hypothesis is based on the investigator’s belief and available information on the population parameters. This means that processing the hypothesis tests can either be done through the two-sample or one-sample parameter tests. The two-sample parameter tests are the best to formulate the hypothesis because they involve setting up the two competing theories (alternate and null hypotheses) (Bashir, 2018). The goodness about this method is that one selects a sample from the multiple samples in various groups to compute the summary statistics to assess the chances that the sample data will support the alternative hypothesis or research.

There are two hypothesis testing types to formulate the research question. For the one-sample t-test, the alternative and null hypothesis are needed because the alternative suggestions assume that there are differences between the comparison value and true meanwhile, the null hypothesis shows no difference (Bashir, 2018). The two-sample parameter test also assumes that one of the three forms relies on the question asked. When the goal is differentiated regardless of the direction of the hypothesis, then the comparison and sample matters will remain the same for each null hypothesis.

The one-sample test is a statistical process that determines how observational samples can be generated through specific methods. For instance, if one is interested in learning if the assembly lines produced computers that are more than five pounds, they can collect a sample from the assembly line, measure how much they weigh and compare with the example of the total value by using the one-sample test (Bashir, 2018).

Types of Data to Generate Various Types of Graphs

There are various types of graphs and charts. The four most common ones include Cartesian graphs, pie charts, bar graphs, and line graphs. For instance, a line graph is a graph that consists of lines to connect two data points that portray the quantitative values for a specific time interval (Azmoodeh, 2014). In this case, the data point markers are used to connect the straight line to help in visualization. The pie charts use the binary data because it calculates the percentages or proportions such as the defective products found in the same sample.

This is done by taking the number of faulty products and then dividing them with the sample data. The bar graph uses ordinal data because of the combination of quantitative and qualitative properties. The ordinal data also enables the researcher to calculate the average scores that contain the quantitative variables (Azmoodeh, 2014). The Cartesian grid graph uses a stair-step approximation of the actual surface. The cartesian graph uses the geological data because of the stair stepping’s coarseness to determine the accuracy of the results, mainly if each problem is hard to derive the conventional rules of thumb of the structures.

Using data to solve a problem or Showcase and Example

Problem-solving is about using imaginative and logical methods to create a sense of a situation to develop an intelligent solution. In such cases, the best problem solver requires active anticipation of future problems to prevent them from developing adverse effects. The problem-solving ability is only addressed by using data (Azmoodeh, 2014). One way this is done is through questions to find if the previous problems were sorted. The issue will be presented through hypothetical situations to see how problem-solving issues can be applied. Yes, the outcome expected achieved if the solution is implemented (Azmoodeh, 2014). The method also enables a person to determine if the problem has been solved or whether another change will be applied in response to the question.

References

Azmoodeh, M. (2014). Abstract data types and program design. Abstract Data Types and Algorithms, 25-47. https://doi.org/10.1007/978-1-349-21151-7_2

Bashir, J. (2018). Hypothesis testing. Scientific Journal of India, 3(1), 62-63. https://doi.org/10.21276/24565644/2018.v3.i1.21

Etikan, I. (2017). Combination of probability random sampling method with non-probability random sampling method (Sampling versus sampling methods). Biometrics & Biostatistics International Journal, 5(6). https://doi.org/10.15406/bbij.2017.05.00148

Goneppanavar, U., Ali, Z., Bhaskar, S., & Divatia, J. (2019). Types of data, methods of collection, handling, and distribution. Airway, 2(1), 36. https://doi.org/10.4103/arwy.arwy_11_19

Schlickel, M. (2012). Research design and data collection. Contributions to Management Science, 45-62. https://doi.org/10.1007/978-3-642-33621-8_3