Statistics is part of mathematics and as Math is essential in every aspect of people’s lives, so is statistics.

Hence, having a varied application in the real world which offers great career opportunities to people; students worldwide opt for this subject.

If you are someone who loves crunching numbers for solving a problem, this is your subject. From machine learning to data sciences; everywhere this subject plays an essential role.

However, frequently it has been seen that scholars like you suffer when it comes to completing assignments.

There are several reasons for that. Time crunch, less information, difficulty understanding and more; all of these give rise to issues which lead to pupils failing to complete a remarkable paper on statistics’ topics.

In this blog, you will go through such problems along with difficult topics where 100% concentration is needed when you work on them. Have a look!

**Descriptive
and inferential statistics **

This is one of the most difficult topics where students often seem to get confused which in turn leads to multiple errors in their assignment.

If you want to do well in this topic of statistics then you will have to understand both aspects in detail along with its difference.

Let’s begin with descriptive statistics!

**Descriptive
Statistics**

This refers to a statistical summary which quantitatively summarizes or defines features of information collected.

However, in sense of mass noun, descriptive statistics is a process through which data is used as well as analyzed.

It is much different than inferential statistics as this aims at summarizing a sample instead of utilizing data for learning about a population which the sample data represents.

Hence, it means that unlike inductive or inferential statistics, descriptive statistics aren’t developed on probability theory’s basis. Also, often it is nonparametric statistics.

Utilizing inferential statistics, professionals draw primary conclusions from data analysis; in such cases, descriptive statistics are also presented.

For instance, if you are reporting about human subjects on paper, a table would have to be included about overall size for essential subgroups such as exposure or treatment group.

Apart from it, you will also, have to provide clinical or demographic characteristics like each sex’s proportion subject, average age, etc.

Particular measures which are used commonly to describe data sets are central tendency’s measure or dispersion or variability measures.

Central tendency measure includes three vital aspects such as mean, median, and mode. However, variability measures involve standard deviation, variables’ maximum and minimum values, skewness, and kurtosis.

**Statistical analysis usage**

This statistic form offers simple summaries of samples along with observations that are made from it.

Such summaries or analysis can either be visual which graphs for easy understanding is or quantitative that is summary statistics.

These summaries can either form basis of data’s initial description which is a part of an extensive statistical depiction. Or they might be sufficient of and in themselves for any specific investigation.

For example, basketball’s shooting percentage is descriptive statistics which summarizes a team or a particular player’s performance. This number refers to the digits which total shots are made divided by total shots taken.

Now let’s say, a player’s shot is 33% which basically means he/she makes one shot in three tries. The percentage which is summarized shows several discrete events.

Also, you will have to consider average grade point. A single number defines s scholar’s general performance across an array of courses he/she experienced.

The use of summary and descriptive statistics has a history which is quite extensive. Also, simple tabulation of economic data and populations was indeed the way through which statistics topic appeared.

Recently, a summarization techniques collection is formulated under exploratory data analysis heading. Box plot is an ideal example of such an approach.

Moreover, in world of business, this statistical form happens to be quite useful as it provides a summary of numerous data types.

For instance, investors, as well as brokers, might use historical return account’s behavior by going through analytical and empirical analysis on investments.

This is performed by them to make a remarkable investment decision in future.

**Inferential
statistics**

It refers to a process which is used for data analysis for deducing probability distribution’s properties.

This infers a population’s properties; for instance, by deriving estimates and testing hypotheses. Also, it’s assumed that observed set of data is generally sampled from a massive population.

Inferential statistics is different from descriptive statistics as the latter one is concerned with observed set of data’s properties. Also, it doesn’t assume that a set of data is from a colossal population.

To define it in simple words, inferential statistics make propositions for a population utilizing data samples drawn from population.

A population’s given hypothesis for which you want to conclude inferences, inductive statistics comprises choosing a single statistical model first whose process will generate required data. Secondly, deducing different propositions from that model.

Conclusion of this process is a statistical proposition. Below are some common statistical proposition forms. Check it out!

First of all, estimating a point which is a specific value that ideally approximates some interest’s parameter.

Also, estimating an interval that is construction of an interval utilizing dataset which is taken from a particular population. In addition, a credible interval is another form that contains values.

Lastly, hypothesis’ rejection along with classification or clustering of groups’ data points.

**Assumptions and models**

All inferential statistics need some assumptions. A model is referred to as assumptions’ set that is concerned with generating similar and observed data.

Statistical models’ description mostly emphasizes interest quantities of a population about which you desire drawing an inference.

Note: Descriptive statistics are used as preliminary moves before drawing formal inferences.

**Degree of assumptions/models**

**Non-parametric
**

Assumptions made in this process which generates data is quite lesser than what you will see in the parametric statistics.

For example, each continuous distribution of probability contains a median that is estimated using sample median.

**Fully
parametric **

This probability distribution can be described as a procedure for generating data which are assumed can be completely described by probability distributions’ family that involves unknown parameters’ finite numbers.

For instance, you might assume that a population values’ distribution is normal with variance and unknown mean.

Also, the data sets are created by random simple sampling. General linear models are widely used along with parametric models’ flexible class.

**Semi-parametric**

This implies typical assumptions which are in between non and fully parametric methods. For instance, you may assume that population distribution has finite mean.

Moreover, you can also assume that mean’s population response level depends on a covariate’s linear manner but not make a parametric assumption that describes variance around mean.

In addition, semi-parametric models are frequently separated into random and structural variation components.

Parametrical treatment is offered to one component and non-parametrical to the other one. Quite popular Cox model is semi-parametric assumptions’ set.

**Valid assumptions/models’ importance**

Whatever assumption level is made, accurately calibrated inference requires such models to be right. That is data generated mechanisms really have to be correctly specified.

Random sampling’s incorrect assumptions can nullify statistical inference. Intricate fully and semi-parametric assumptions can also be a concerned cause.

For instance, incorrect assumption of Cox model will lead to unsuccessful conclusions in several cases. Also, such normality assumptions in population invalidate certain forms of inference which is regression based.

Usage of a parametric model is skeptically viewed by numerous professionals in human populations’ samples.

A normal distribution might be completely unrealistic and unwise assumption one can make if you are dealing with economic population.

According to the central limit theorem, distribution of sample means that it is normally distributed if this distribution isn’t tailed heavily.

Scholars often get confused which leads to errors when completing their assignments. This is just the basics of Descriptive and inferential statistics.

When going in-depth, pupils confuse one with another and come up with a conclusion which is often a mistake on their part.

If you want to avoid struggling on this topic, then you should pay extra attention to these topics when going through the Statistics course.

**Key
differences between the two **

To get a better idea and differentiate between these two, you should know the key differences. Have a look below!

Descriptive statistics refers to a discipline that deals with defining a population under a study. However, inferential statistics focuses on getting conclusions about a population depending on observation and sample analysis.

Descriptive statistics in a useful way collects, analyses, organizes and presents information or data. On the other hand, statistical inference tests hypothesis, compares data, along with predict future results.

There is a tabular or diagrammatic representation of ultimate outcome in the descriptive statistic approach. However, in inferential statistics final result is concluded in the probability form.

Statistical inference explains likelihood of an event occurring whereas descriptive approach simply describes any situation.

Descriptive method explains information which is knowing for summarizing sample. However, inferential approach attempts at reaching conclusion for learning about population. This extends beyond available data.

Now that you are aware of the key difference between these two, you should not have any problem with the basics of this topic.

**Issues
with writing assignments**

There are numerous issues which scholars like you might face when working on this topic. However, these dilemmas arise due to three things primarily; understanding, reference material, and time.

Guessing you are a little bit still confused! Don’t worry keep reading.

First of all, understanding this topic is the most difficult of all. Unless you go through it in detail and have a clear idea about every detail, you will face a problem when you sit to complete your assignment.

One small doubt will hinder your work pace along with quality of work. Moreover, the final outcome or result will definitely be wrong if doubts lurk in your mind.

Hence, it is necessary to understand the entire concept along with different models. Also, it is quite essential to know the major differences between these two as it will help one to differentiate and answer properly in their paper.

Next issue which you will face when working on this topic or something related to it is finding remarkable materials.

You might think that with the help of the internet you will be able to get everything you need. However, it’s not always the case.

You might get a lot of reference material or very less. If information is less to none then you will have difficulty even writing a few pages for your project.

Now let’s say you got ample reference material and data on this topic; how will you know which one to incorporate in your paper for better grades.

All these questions and confusions make it quite difficult for pupils to complete project on time. However, even if you complete it, it most probably won’t be up to the mark which can fetch you high scores.

Last problem you will struggle with is time!

Being a student, you already are aware that how less time you have to go through the complete syllabus to prepare for your exam.

Apart from that, there is an extracurricular activity, personal life, taking rest, and more which also require time.

In such a scenario, how can you give more time to your assignments? It’s not like you can get more than 24 hours in a day.

Within such a short time it is not possible for most to gather all materials, have a clear concept on every notion related to the topic, and still, ample time left to finish the work.

These are the most common issues which you will face when writing assignments. However, you can easily overcome these by taking help from your classmates, college professors, private tutors, and whoever who can assist you in completing your work.

**Author Bio:
**

Evelyn W. Minnick is quite a favorite of the students as she makes even the hardest of topic quite simple to understand. Apart from being a teacher, she is a blogger who posts simple solutions to challenging topics. She completed her MBA from Yale School of Management and has over six years of experience in this field which makes her one of the best people to take assistance from.