Factors We Consider When Conducting Inferential Statistics

After collecting data and conducting descriptive statistics to summarize and understand the dataset, one may require help to conduct inferential statistics with the aim of estimating population parameters or hypothesis testing to draw conclusions based on sample population data.

The sample's characteristics can be generalized to the broader population from which the dataset was obtained. A truly random sample is imperative when estimating the parameters of a population. If random samples are not representative of the study population, valid statistical inferences cannot be drawn or correct testing of hypotheses conducted.

This article contains details about the factors considered during the selection and running of the inferential statistics conducted by experts in our company.

While descriptive statistics such as the mean and standard deviation provide graphical or numeric summaries or descriptions for variables, inferential statistical analysis allows one to create inferences and draw conclusions from a sample data to a larger population from which the sample was drawn. The statistical analyses rely on random sampling techniques to derive estimates pertaining to a particular population parameter.

The random sample size to be used in data analysis and the statistical tests to be conducted depend on various factors such as the research questions, study designs, and objectives. A representative sample helps in studying particular population values without having to collect data from every member of the population.

Having collected data from a sample, we can provide help to conduct inferential statistics to scholars, students, and researchers at different academic and professional levels to understand the larger population from which the sample was drawn. Through our services, one can confidently present the data analysis chapter of their paper containing logical statistical inference(s) about the population parameter of interest. Below are some of the factors we consider when conducting inferential statistics.

1. Research questions

The various types of study questions affect the choice of inferential statistics to draw conclusions pertaining to an entire population from the findings obtained from sample data.

A relational question focuses on determining relationships among variables and the choice of inferential statistics in such a situation will be that which helps in calculating associations. If the question is causal in nature, the researcher may be interested in finding out information about the effect of an intervention/treatment on a particular outcome variable, therefore, determining the difference.

2. The study design

The study design depends on what is being measured and is further influenced by the type of research question asked. In a question of association where one is interested in determining relationships between variables, a study design to measure agreement is most appropriate. Correlation tests can be used when examining the strength of a relationship between two variables.

Regression analysis can also be used to measure the strength of relationships or connections between two variables, where, one variable is considered the outcome(dependent) and the other is the predictor (independent variable). The regression analysis is mostly used when determining the effect of multiple independent variables on a dependent variable at the same time.

3. Types of population estimates

When estimating population parameters from sample statistics, we consider both interval and point estimates. A point estimate represents a single estimate of a particular parameter in the study population. A sample mean for instance can be described as the point estimate for a population mean. An interval estimate such as the confidence interval provides a range of values between which the parameter under study lies.

Confidence intervals take into account the sampling errors when estimating parameters and reveal the uncertainty of the point estimates when used together. In our help to conduct inferential statistics, we ensure that random sampling and a suitable sample size that truly represents the study population are taken into account. A sample statistic can correctly represent the population when estimating parameters only if unbiased sampling is practiced. Both point and interval estimation are important when choosing a statistical test for a dataset.

4. The levels of measurements

According to the level of measurement, variables can be grouped into nominal, ordinal, or interval estimates. When the independent variable is nominal, we must consider the dependent variable's level of measurement before determining the type of inferential statistics to use; with an exception of regression and correlation analyses.

The correlation analysis determines the strength of the relationship between two variables and, therefore, the levels of measurement for both variables must be established. Additionally, regression analyses may be applicable for several independent variables with different levels of measurement, hence, our experts will have to consider the levels of measurement for each dependent variable.

5. Distribution of data

It is imperative to establish how data are distributed especially when selecting inferential statistics for interval-level variables. The inferential statistics conducted by experts from our company entails determining whether the dataset conforms to the basic assumption of the normal distribution when using parametric tests in estimating population parameters and hypothesis testing.

The analysis of variance (ANOVA) is a parametric test used in comparing the means for three or more groups defined by one or more variables. The one-way ANOVA is useful in determining the groups whose means are to be compared in a similar manner in which the students' t-test statistic is used when comparing the means for two groups. Repeated-measures ANOVA is useful when analyzing the difference between the means of three or more measures from the same group of participants.

If the data are not normally distributed or are skewed, we use nonparametric tests to draw statistical inferences from a sample to a whole population of interest. Alternative nonparametric tests for one-way ANOVA and repeated measures ANOVA include the Kruskal Wallis one-way ANOVA and the Friedman ANOVA.

6. Level of significance for the statistical analysis

Inferential statistics can also be used to calculate the p-value or the probability that an obtained difference is by chance. A comparison between the p-values and the predefined levels of significance demonstrates the acceptable level of error for the quantitative data. To determine the presence of a statistically significant relationship between the rows and columns of a contingency table, we use nonparametric tests such as the Chi-square.

7. Sampling error in inferential statistics

Sampling errors occur when some of the population values are not captured in the sample data. A sampling error is a difference between the true population values called parameters and the measured sample values (statistics). We use probability sampling techniques to reduce uncertainties in inferential statistics, thus, minimizing the chances of sampling errors arising.

One can rest assured of the best results after making the decision to hire a statistician to conduct inferential statistics from our company. The experts understand the difference between a descriptive statistic; a measure to describe a sample such as a sample standard deviation or mean; and a parameter, which is a measure used in describing the whole population. The population standard deviation and mean are some of the parameters we consider when conducting inferential statistics.

8. Compliance of the dataset with the basic assumptions

Performing a hypothesis test is a formal statistical analytical process that makes use of inferential statistics. The main objectives of hypothesis testing include comparing two or more groups or populations and determining and assessing relationships between variables through the use of samples. After understanding the objectives of hypothesis testing using statistical tests, we must also evaluate whether the dataset presented for analysis is completely compliant with the basic assumptions. These include:

• Normal distribution for scores in the population from which the sample was drawn.
• The representative sample size is large enough to reflect the larger population.
• Similar variances or measures of spread for the groups being compared.

After establishing whether the sample data comply with the assumptions or not, our experts make a decision on whether to use parametric or nonparametric tests. The inferential statistics can be categorized into correlation, regression, or comparison tests which we use depending on what is being compared and the number of samples, variables, or groups involved.

9. Clinical significance of the study results

The level of clinical significance determines the real value of a study's findings. While the statistical significance is fundamental in determining whether the results were true or obtained by chance, the clinical significance explains the usability of the study's results and their effects in real-world activities. Therefore, after offering clients help to conduct inferential statistics, we can also assist in determining the clinical significance and the real-world importance when interpreting the obtained results.

All these factors form the foundation for our inferential statistical analyses, depending on what the client wants. Those who purchase the services of a statistician to conduct inferential statistics from our credible company have the assurance of excellent service from experts.

We are available and glad to help in any subject at all academic and professional levels where data analysis services may be required. Our clients are guaranteed high-quality services with an excellent and friendly customer support team. One can place orders, inquiries, consultation, or track work progress at any time they may need to. The company has a strict team of editors who ensure that flawless clients' orders are delivered on time.