What is the difference between inferential and differential statistics

Viewed k times. However, the wikipedia page for statistical inference states: For the most part, statistical inference makes propositions about populations, using data drawn from the population of interest via some form of random sampling.

Improve this question. Jeromy Anglim Inferential methods allow to assess properties of this mechanism based on the data. Example: You want to verify an electro-physical formula based on outcomes that can be measured only approximately or under imperfect conditions.

Add a comment. Active Oldest Votes. In this context the distinction is that : Descriptive statistics are functions of the sample data that are intrinsically interesting in describing some feature of the data. Classic descriptive statistics include mean, min, max, standard deviation, median, skew, kurtosis.

Inferential statistics are a function of the sample data that assists you to draw an inference regarding an hypothesis about a population parameter. Inferential statistics: Standard test statistics like t and z, for a given data generating process, where the null hypothesis is false, the expected value is strongly influenced by sample size. Most researchers would not see such statistics as estimating a population parameter of intrinsic interest. Descriptive statistics : In contrast descriptive statistics do estimate population parameters that are typically of intrinsic interest.

For example the sample mean and standard deviation provide estimates of the equivalent population parameters. Even descriptive statistics like the minimum and maximum provide information about equivalent or similar population parameters, although of course in this case, much more care is required. Furthermore, many descriptive statistics might be biased or otherwise less than ideal estimators. However, they still have some utility in estimating a population parameter of interest.

So from this perspective, the important things to understand are: statistic : function of the sample data parameter : function of the population data generating process estimator : function of the sample data used to provide an estimate of a parameter inference : process of reaching a conclusion about a parameter Thus, you could either define the distinction between descriptive and inferential based on the intention of the researcher using the statistic, or you could define a statistic based on how it is typically used.

Improve this answer. Jeromy Anglim Jeromy Anglim I guess I have started with the assumption that a statistic is a function of the data. But perhaps you are alluding to the point that we often think of inferential statistics as the broader set of techniques used to do inference? I guess I was thinking that such statistics are used in an inferential process e. I've often seen textbooks use these examples.

But I suppose the p-value and the binary inference itself could be seen as statistics i. And the binary inference itself could be seen as most clearly aligned to the inference. Is that what you are getting at? So from a frequentist perspective, t, p, and the binary inference are all random variables. All were involved in the inferential process. I'm not sure what the pros and cons are of labelling all or only some such statistics as inferential. Show 2 more comments.

Matt Krause Matt Krause Inferential statistics is the branch of statistics that concludes by analyzing a sample from a whole lot of a particular pattern. Inferential statistics is generalizing a particular fact to the whole lot by examining a sample of it. The deduction of the result from the sample is judged the same for the whole group.

It is indeed a very convenient way when a large number of numbers or population cannot be examined for a particular cause. The sample chosen must be exactly from the whole lot and the result of the sample will directly apply to the whole lot.

Mostly, inferential statistics work with probability theory. The methods used in inferential statistics is the estimation of parameters and testing of hypothesis.

The propositions made out of the sample become models and the same model is subjected to the entire community. The models of inferential statistics are many, it involves the approximate distribution of the sample data. But all the models arrive at one particular conclusion but used in different scenarios. Inferential analysis can be used to check what the entire population might think about the new government. This can happen just by checking with a few thousands of people.

The statistical analysis is a perfect way to find solutions to many ongoing as well as future problems. Though the descriptive statistics offer only direct data which may be already known. How it is presented gives way to many explorations of information. This exploration gives rise to inferential statistics. The inferential statistics gives wonderful conclusions about a lot of facts.

This is an easier way to arrive at a conclusion where the numbers available are larger than larger. The focus on making assumptions in inferential statistics is also logical, so mistakes are a matter of probability. Which one to use when the researchers know it very well. Skip to content The collection, organization, and analyses of data are called Statistics.

The difference between descriptive and inferential statistics can be drawn clearly on the following grounds:. So, we have enough discussion on the two subjects, all you need to know is that descriptive statistics is all about illustrating your current dataset whereas inferential statistics focuses on making assumptions on the additional population, that is beyond the dataset under study.

While descriptive statistics provide the summation of the data the researcher has actually studied whereas inferential statistics, makes the generalisation, which means the data provided to you is not actually studied. Bundle of thanks , Its a really helpful for me to understand basic concept of descriptive and inferential statistical analysis with comparison. Thanks…it helps a lot.. Your email address will not be published.

Save my name, email, and website in this browser for the next time I comment. Key Differences Between Descriptive and Inferential Statistics The difference between descriptive and inferential statistics can be drawn clearly on the following grounds: Descriptive Statistics is a discipline which is concerned with describing the population under study.

Inferential Statistics is a type of statistics; that focuses on drawing conclusions about the population, on the basis of sample analysis and observation. Descriptive Statistics collects, organises, analyzes and presents data in a meaningful way. On the contrary, Inferential Statistics, compares data, test hypothesis and make predictions of the future outcomes. There is a diagrammatic or tabular representation of final result in descriptive statistics whereas the final result is displayed in the form of probability.

Descriptive statistics describes a situation while inferential statistics explains the likelihood of the occurrence of an event. Descriptive statistics explains the data, which is already known, to summarise sample.