What is the relationship between inferential and descriptive statistics? - Quora
Descriptive and inferential statistics each give different insights into the Probability distributions, hypothesis testing, correlation testing and. The primary difference between descriptive and inferential statistics is that descriptive statistics is all about illustrating your current dataset. The term “descriptive statistics” is used to mean two slightly different things in statistics books. When we contrast descriptive versus inferential use of statistics.
What the relationship between descriptive and inferential statistics? | Socratic
Our data is the information collected from the population. Population is always defined first, before starting the data collection process for any statistical study. Population is not necessarily be people rather it could be batch of batteries, measurements of rainfall in an area or a group of people. It is the part of population which is selected randomly for the study. The sample should be selected such that it represents all the characteristics of the population.
The process of selecting the subset from the population is called sampling and the subset selected is called the sample.
Difference Between Descriptive and Inferential Statistics (with Comparison Chart) - Key Differences
Data can be summarized and represented in an accurate way using charts, tables and graphs. We have marks of students and we may be interested in the overall performance of those students and the distribution as well as the spread of marks.
Descriptive statistics provides us the tools to define our data in a most understandable and appropriate way. Inferential Statistics It is about using data from sample and then making inferences about the larger population from which the sample is drawn. The goal of the inferential statistics is to draw conclusions from a sample and generalize them to the population.
It determines the probability of the characteristics of the sample using probability theory. The most common methodologies used are hypothesis tests, Analysis of variance etc. With hypothesis testing, data from studies are used to make a judgement about whether enough information exists to support a conclusion that a relationship exists between variables.
To make this judgement, two types of hypotheses are considered, the research hypothesis which in this case will be either directional or non-directional and the null hypothesis. To introduce you to the idea of hypothesis testing, consider the following research situation.Lesson 3 - What is Descriptive Statistics vs Inferential Statistics?
The research hypothesis is: Boys will have higher scores than girls on the mathematics test. The null hypothesis is Ho: There will be no difference in mathematics test scores between boys and girls.
In this example, Ha is the research hypothesis H indicates hypothesis, the letter "a" indicates that this is the alternative or research hypothesis and Ho is the null hypothesis H is hypothesis, "o" is the symbol for null. When making a scientific judgement about this study, hypothesis testing is used. In all types of hypothesis testing, the null hypothesis is always the hypothesis tested.
- Difference Between Descriptive and Inferential Statistics
The logic of testing the null hypothesis follows something like this: The null indicates no difference in math scores between boys and girls in this example. If the null hypothesis can be rejected, then some hypothesis other than the null must be accepted which will be either the directional or non-directional. Note that only the null indicates no difference, and both the directional and non-directional hypotheses indicate some difference between boys and girls on math scores.
So by rejecting the null hypothesis, we can establish evidence for a difference between boys and girls. So in Study 1, we will first collect data, then we will use a statistical procedure which are discussed below to test the null hypothesis.
What the relationship between descriptive and inferential statistics?
If the null hypothesis is rejected, then we can accept the alternative or research hypothesis and state that sex and math scores are related in some way maybe that boys score higher than girls. If the null hypothesis is not rejected, then we state that there is not enough evidence in the data collected to say that a difference exists between boys and girls on math test scores.
The key to the conclusion we draw is whether the null hypothesis is rejected or not rejected. If rejected, then a relationship or difference exists, if not rejected, then sex and math scores are not related.
Intelligence and Math A researcher wishes to know if there is statistical evidence that one's intelligence is related to one's performance on a mathematics test.
Those with higher levels of intelligence will score higher on a mathematics test. There is no relationship between intelligence and mathematics test scores. For Study 2 we are interested in knowing whether a relationship exists between two variables, intelligence and math scores. As with Study 1, the question of interest is whether the null hypothesis can be rejected.
As before, the null hypothesis is tested. If the null hypothesis is rejected, based upon the evidence provided in the data collected, then we can say that intelligence and math scores are related perhaps positively related. If the null hypothesis is not rejected, then we say that there is not enough evidence in the data to say that intelligence and math scores are related in any way. Statistical Significance Statements of Significant As noted above, when performing statistical testing of hypotheses, one always tests the null hypothesis.
There are many statistical methods for testing null hypotheses, and a number of them are discussed below e. All statistical testing procedures have corresponding null hypotheses. When testing hypotheses with statistical tests like the t-test, ANOVA, and Pearson's r, one must assume that the null hypothesis is true; that is, one must assume that no differences actually exist among the groups examined, or that no relationships exist among the variables.
All of the statistical tests will provide some indication as to whether evidence provided by the data support or do not support the null hypothesis.
Descriptive and Inferential Statistics
If the evidence indicates that the null hypothesis is supported, then the researcher concludes that no differences between the groups really exist or that no relationship among the variables existsand the researcher can therefore state that the statistical results indicate that there were no statistically significant differences among the groups, or that there were no statistically significant relationships among the variables.
If, however, the data do not support the null hypothesis, then the researcher can reject the null hypothesis and accept the research hypothesis. If the data do not support the null hypothesis, then the researcher can say that a statistically significant difference exists among the groups which is, of course, what the research hypothesis claimsor that a statistically significant relationship exists among the variables.
Thus, when one claims that results are statistically significant, this does not mean that important results were obtained; rather, it simply means that the data do not support the null hypothesis. So, what does it mean when one hears, say in the news, that researchers found a significant relationship between smoking and lung cancel, or that researchers found a significant relationship between leaving a light on for young children at night and their likelihood of developing near-sightedness?
Does the term significant mean they found an important result?
In this case significant only means that the data collected by the researchers provided enough evidence to reject the null hypothesis, but this does not mean that something important was found. To illustrate the difference, consider a study of computer-assisted instruction in mathematics. A recent study found a significant difference in math achievement scores between two groups: One might be tempted to conclude that since a significant difference was found, the computer-assisted instruction makes a big difference.
This is not the case.