Descriptive vs Inferential Statistics: Whats the Difference?

Median and Mode are not affected by extreme values (i.e., by outliers), whereas Mean is affected by outliers. Connect with an advisor to discuss career outcomes, curriculum, and get your questions answered. The DNP-FNP track is offered 100% online with no campus residency requirements.

Step 9. Report the results of statistical analysis

For example, nurse executives who oversee budgeting and other financial responsibilities will likely need familiarity with descriptive statistics and their use in accounting. Descriptive statistics can also come into play for professionals like family nurse practitioners or emergency room nurse managers who must know how to calculate variance in a patient’s blood pressure or blood sugar. Moreover, in a family clinic, nurses might analyze the body mass index (BMI) of patients at any age. The relevance and quality of the sample population are essential in ensuring the inference made is reliable.

1. It helps to identify and quantify the strength and direction of the association between variables and to predict the dependent variable’s value for given independent variable values.
2. You cannot use the data you have collected to generalize to other people or objects (i.e., using data from a sample to infer the properties/parameters of a population).
3. When we want to summarize and find patterns within the same variable, then use univariate analysis to know the relationship between two variables, referred to as bivariate analysis.
4. In the context of an experimental study, the dependent variable (also called outcome variable) is directly linked to the primary outcome of the study.
5. Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria).
6. The problem is that some extreme values (outliers), like “’86,” in this case can skew the value of the mean.

Other terms sometimes used synonymously include blocking variable, covariate, or predictor variable. Confounding variables are extra variables, which can have an effect on the experiment. They are linked with dependent and independent variables and can cause spurious association. For example, in a clinical trial for a topical treatment in psoriasis, the concomitant use of moisturizers might be a confounding variable. A control variable is a variable that must be kept constant during the course of an experiment. They’re still based on the data you have available, but with a broader focus, extending your findings to predict, imagine, or hypothesize about how a much larger sample or population would appear.

The mode as in our case may not necessarily be in the center of the distribution. Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data. Another form of presenting frequency distributions is the “stem and leaf” diagram, which is considered to be a more accurate form of description.

( Five-point Summary

The problem is that some extreme values (outliers), like “’86,” in this case can skew the value of the mean. In this case, we consider other values like the median, which is the point that divides the distribution into two equal halves. It is also referred to as the 50th percentile (50% of the values are above it and 50% are below it). In our previous example, since we have already arranged the values in ascending order we find that the point which divides it into two equal halves is the 8th value – 42. In case of a total number of values being even, we choose the two middle points and take an average to reach the median. For example, you could apply inferential statistics to a small sample of human data in order to make trends or suggest hypotheses about a vastly larger amount of people.

A measure of the relationship between the two variables that are not affected by the units of measurement is known as the correlation coefficient. What this measure tells us is the relative strength of a linear relationship between two variables that are numerical in nature. Covariance cannot tell whether the value indicates a strong or weak relationship because covariance can take any value, and the value of the covariance depends on the units of measurement of the variables x and y.

An independent variable influences, affects or predicts a dependent variable. These methods help to provide a clear and concise summary of the data, facilitating easier interpretation and understanding. Statistics can be broadly divided into descriptive statistics and inferential statistics.3,4 Descriptive statistics give a summary about the sample being studied without drawing any inferences based on probability theory. Even if the primary aim of a study involves inferential statistics, descriptive statistics are still used to give a general summary.

Basics of statistics for primary care research

These steps assume necessary background activities, such as conducting literature review and writing clear research question or aims, are already complete. Sir Ronald Aylmer Fisher, a British Genius, is widely considered as the father of modern statistics. The DNP-Leadership track is also offered 100% online, without any campus residency requirements. This program involves finishing eight semesters and 1,000 clinical hours, taking students 2-2.7 years to complete if they study full time. Ordinal variables or ranked variables are similar to categorical, but can be put into an order (e.g., a scale for severity of itching). Early stage AI lab based in San Francisco with a mission to build the most powerful AI tools for knowledge workers.

A frequency distribution can show the number or frequencies of the observations in numbers or percentages in a class interval. These class intervals must be non-overlapping, so they must be mutually exclusive and exhaustive. A bar and pie chart can visually represent the relative and percent frequencies. An example of an inferential statistic is the calculation of a confidence interval.

It gathers the same sales data above, but it uses the information to make predictions about what the sales of the new hot sauce will be. The act of using descriptive statistics and applying characteristics to a different data set makes the data set inferential statistics. We are no longer simply summarizing data; we are using it to predict what will happen regarding an entirely different body of data (in this case, the new hot sauce product).

1. However, inferential statistics are designed to test for a dependent variable — namely, the population parameter or outcome being studied — and may involve several variables.
2. A bar and pie chart can visually represent the relative and percent frequencies.
3. We estimate the speed of the vehicle approaching while driving or crossing a road.
4. For example, a population census may include descriptive statistics regarding the ratio of men and women in a specific city.
5. The DNP-Leadership track is also offered 100% online, without any campus residency requirements.

Graphical Representation

A note is that for estimation type of studies/surveys, sample size calculation needs to consider some other factors too. The other factor is the “margin of error” – the amount of deviation which the investigators find acceptable in terms of percentages. Regarding confidence levels, ideally, a 95% confidence level is the minimum recommended for surveys too. Finally, we need an idea of the expected/crude prevalence – either based on previous studies or based on estimates.

Descriptive statistics are an additional way to check for errors and ensure data are ready for analysis. While not discussed in the communication assessment exemplar, the authors did prepare descriptive vs inferential statistics data for analysis and report missing values in their descriptive statistics. We can visualize the relationship between the population, sample, descriptive statistics, and inferential statistics (see figure below). We are typically interested in a population of interest but may not be able to collect data from the entire population because of budget, time, access, or other constraints.

We use the measure below to understand and see the relationship between two variables. X is often used to represent an independent variable and Y is often used to represent a dependent variable. In addition, you are likely to see Greek letters like μ (pronounced mu), which signifies the population mean. The statistical symbols that represent the population are different from those that represent the sample. For example, the population mean has a different symbol than the sample mean. Descriptive statistics is used to describe and organize data while inferential statistics draw conclusions about the population from samples by using analytical tools.