Types of Statistics: Descriptive and Inferential Statistics
The several disciplines of statistics that are used to characterise, condense, and evaluate data are referred to as types of statistics. Data must be categorised according to certain properties since they can be subjected to a wide range of processes.
These statistical methods facilitate the systematic collection of data and the subsequent derivation of conclusions from it as a statistics dissertation writing service.
Descriptive statistics and inferential statistics are the two main categories of statistics that are talked about in the best dissertation help platforms. This article will teach us more about these statistical approaches, the many kinds of data that are utilised, the variables that go along with them, and some related instances.
What Kinds of Statistics Are There?
Data collection, description, organisation, analysis, and interpretation all need the use of distinct statistical types.
By examining 5 examples of descriptive statistics, these statistics may be used to estimate the parameters of the population and identify specific characteristics associated with the data. The two categories of statistics are listed below.
Descriptive Statistics
Descriptive statistics is mostly used as business research methods and statistics using spss pdf versions and therefore it is one kind of statistics. It offers summaries of the population or the sample and is used to quantitatively characterise the properties of the known data.
Descriptive statistics can be shown using tables, charts, and graphs. The following are the descriptive statistics measures: (Indeed Editorial , 2023)
- Central tendency measures: These are used to characterise data in relation to one central point. Three different forms of central tendency exist: mean, median, and mode.
- Dispersion metrics: These metrics are used to characterise the data’s variability. Stated differently, its purpose is to measure the degree to which a distribution deviates from a central value.
This group includes the following types: range, variance, standard deviation, mean deviation, quartile deviation, and coefficients of dispersion.
Example of Descriptive Statistics
A pie chart that shows the various hair colours in the population and makes it evident that brown hair is the most frequent would be an excellent example.
Typical descriptive statistics instruments
The following statistical measures are commonly used in descriptive statistics to characterise groups:
- Central tendency: To find the dataset’s centre, use the mean or median. You can see where most values fall using this metric.
- Dispersion: How far do the data reach from the centre? To calculate the dispersion, utilise the range or standard deviation. The values cluster closer to the centre when the dispersion is minimal. Data points that are more dispersed fall farther from the centre. Additionally, the frequency distribution can be graphed.
- Skewness: The metric indicates if the value distribution is symmetric or skewed. Refer to: Inequitable Distributions (Hillier, 2023)
Inferential Statistics
When inferences about the population must be formed and conclusions must be reached using the given samples, inferential statistics are employed. Inferential statistics draws conclusions about the population data by applying a variety of statistical tests and sampling strategies. The following list contains the inferential statistics measures:
- Testing hypotheses: Using an estimated sample size, this technique is used to verify certain assumptions and draw conclusions about the characteristics of the population.
For this, a wide variety of statistical tests are employed. The z test, t test, f test, and ANOVA test are a few examples.
- Regression Analysis: This kind of analysis is applied when it’s necessary to assess and quantify how changes in one variable affect changes in another. Regression analysis comes in the following flavours: logistic, nominal, simple linear, multiple linear, and ordinal. (Simplilearn, 2023)
Example of Inferetial Statistics
In order to anticipate the most popular hair colour over the entire college, this would involve analysing the hair colour of a single class of students.
Types of Inferential Statistics
- Regression Analysis
The link between a collection of independent factors and a dependent variable is displayed using regression models.
Using this statistical technique, you may forecast the dependent variable’s value based on the values of the independent variables. To ascertain if the associations seen in the sample data are true in the data set, hypothesis tests are included.
- Hypothesis Tests
To compare complete populations or evaluate the correlations between variables using samples, hypothesis testing is utilised. To make reliable conclusions, predictions or hypotheses are put to the test using statistical tests.
- Confidence Intervals
Estimating population parameters, which are often values that are unknown or unknowable, is the primary objective of inferential statistics.
In order to calculate an interval estimate for a parameter, a confidence interval looks at the variability in a statistic. Confidence intervals provide a range of values within which the estimated population number falls by accounting for sampling error and uncertainty.
Every confidence interval has a corresponding confidence level, which expresses the likelihood, expressed as a percentage of the interval, that the parameter estimate will be present if the investigation is repeated.
Summary
The meaning of descriptive statistics is interpreted using this kind of statistical analysis. This implies that we utilise these statistics to explain the significance of the obtained data once it has been gathered, processed, and summarised.
Alternatively said, it’s employed when drawing inferences from data that is subject to random fluctuations like sample variance, observational mistakes, etc.
Using data from a sample, inferential statistics is a technique that enables us to draw conclusions, forecasts, or judgements about the population as a whole.
It gives us authorization to make claims that go beyond the facts or information that is now accessible. For instance, estimating using research that is fictitious.
In descriptive statistics, the provided observations serve as a summary of the data. A population sample is used to create the summary, which is based on metrics like the mean or standard deviation.
A set of data can be arranged, represented, and described using tables, graphs, and summary measures in descriptive statistics. Consider a group of people in a city who watch television or use the internet.
Works Cited
Indeed Editorial , 2023. Inferential Statistics: Definition and Pros and Cons. [Online]
Available at: https://ca.indeed.com/career-advice/career-development/inferential-statistics
[Accessed 29 February 2024].
Hillier, W., 2023. What’s the Difference Between Descriptive and Inferential Statistics?. [Online]
Available at: https://careerfoundry.com/en/blog/data-analytics/inferential-vs-descriptive-statistics/
[Accessed 29 February 2024].
Simplilearn, 2023. Descriptive vs. Inferential Statistics: Key Differences and Measurement Techniques. [Online]
Available at: https://www.simplilearn.com/difference-between-descriptive-inferential-statistics-article
[Accessed 29 February 2024].
