Question

Question 6 What is the difference between descriptive and inferential statistics? Provide an example to each. Refer to Week 9 lecture slides and recordings for further information (Must provide 2 references, there should be at least one academic reference and one non-academic). (At least 200 words).

Answer 1

What is descriptive statistics?

Descriptive statistics, also known as samples, can determine multiple observations you take throughout your research. It's defined as finding group members that fit the parameters of your research, noting data about groups you're testing and the application of statistics and graphs to conclude the findings from this group. In other words, you're paring down the results from this group and reducing them to a few key points.

In this case, you're only trying to test for results you can get from relevant individuals. This requires you to continue testing if your results affect a larger portion of the population.

Some conclusions you can measure for include:

• Central tendenc**y:** The process of using the mean and the median to determine the location of your data points on a graph.
• Dispersion: Dispersion is another way to identify the severance of data points from the center of your graph. A small number means that the dispersion is closer to the center while the larger number implies a greater separation from the epicenter of the graph.
• Skewness: Skewness highlights the separation of the data points you measured from each other. You can conclude if they're symmetrical or skewed from your measurements.

For example, if we had the results of 100 pieces of students' coursework, we may be interested in the overall performance of those students. We would also be interested in the distribution or spread of the marks. Descriptive statistics allow us to do this.

What is inferential statistics?

Inferential statistics is when you take data from a sample group and make a prediction that impacts the conclusion on a large population. You can use random sampling to evaluate how different variables can lead to you make generalizations to conduct further experiments. To get an accurate analysis, you'll need to identify the population you're measuring, create a sample for that population and incorporate analysis to find a sampling error.

A few ways you can measure for inferential statistics include:

• Hypothesis tests: Hypothesis tests determine if the population you're measuring has a higher value than another data point in your analysis. It can also conclude if populations vary, which is centered on the results you earned from multiple experiments.
• Confidence intervals: Confidence intervals discover the margin of error in your research and if it affects what you're testing for. You'll mainly have to estimate for the range a population can fall under for mean and median calculations.
• Regression analysis: A regression analysis is an association between the independent and dependent variables of an experiment. You can perform a regression analysis after you know the results of the hypothesis test, so you know the relationship of the subject matter. A few things you can test for is the comparison between two populations or the height and weight of different genders.

For example, you might be interested in the marks of all students in the UK. It is not feasible to measure all  marks of all students in the whole of the UK so you have to measure a smaller sample of students (e.g., 100 students), which are used to represent the larger population of all UK students. Properties of samples, such as the mean or standard deviation, are not called parameters, but statistics. Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were drawn.

BASIS	DESCRIPTIVE	INFERENTIAL
The calculation of certainty	Descriptive statistics only measure the group you assign for the experiment, meaning that you decide to not factor in variables.In other words, you're more likely to get a definitive calculation with descriptive statistics.	Inferential statistics account for sampling errors, which may lead to additional tests to be conducted on a larger population depending on how much data is needed.
Measure	The primary difference between descriptive and inferential statistics is that descriptive statistics measure for definitive measurement	while inferential statistics note the margin of error of research performed. You'll need to account for the deadlines you have for research and development to choose which statistic is more viable for you.
The simplicity of calculations	to come up with conclusions for descriptive statistics is more difficult. Simplicity is ideal for when you need quick results that meet a particular deadline for your experience or testing period.	Considering that you're only testing for variables with inferential statistics, it's easier

ACADEMIC REFRENCE

DESCRIPTIVE - marks obtained by students

INFERENTIAL - forecasted number of Lunch to be prepared

NON ACADEMIC REFRECNE

DESCRIPTIVE - MEAN VALUE OF SHARES FOR 10 YEARS

INFERENTIAL - AVERAGE RAINFALL PER ANNUM