By ECZ Study Tool, May 6, 2023
Statistics is a mathematical topic that deals with the collection analysis interpretation presentation and organization of data. It is an important subject for students as it helps leaners make sense of the amount of data they encounter in their daily lives. This article will discuss the fundamentals of statistics which include data collection analysis interpretation presentation and organization.
View a statistics tutorial here
Data
Collection
Is the process of gathering information from different set of data there are two types of data namely primary and secondary a primary data is collected directly from the set of data while secondary data is obtained from existing data.
Primary data can be
collected through observation survey and experiments. Observational data is
collected by observing events or behavior without interfering in any way.
Secondary data can be
obtained from existing sources such as books journals and websites or through government
reports. Hence the advantage of secondary data is that it is already collected
and it can save time and money.
Data Analysis
Is the process of organizing interpreting and drawing conclusions from data. There are two types of data analysis namely descriptive and inferential.
Descriptive analysis involves summarizing the data using measures such as mean median mode range and standard deviation. These measures provide an over view of the data and help to identify patterns and trends.
Inferential analysis
involves making conclusions or predictions about a larger population based on a
sample this type of analysis uses statistical methods like hypothesis testing
and confidence intervals.
Data
Interpretation
Data interpretation
involves making sense of the data by drawing conclusions and making inferences
therefore this process involves looking at patterns trends and relationships in
the data.
A common method of interpreting
data is creating a graphs which are helpful to visualize the data and make it easier
to identify trends and another is to use statistical models this can help to
identify relationships between variables and make predictions about future
events.
Data
Presentation
Is the preparing of data in a clear way and easy for everyone understand through this exercise the goal of data presentation is to communicate the key findings of the data that has been analysis.
A common method for presenting data is the use of tables and graphs. A tables can be used to display raw data while graphs can be used to display trends and patterns.
Another technique for
presenting data is to use graphs which are visual representations of data to
communicate information quickly and clearly.
Data
Organization
Is the process used in
statistics which involves the arranging of data in a way that makes it easy to understand
there are several methods for organizing data namely databases and
spreadsheets.
A databases are
electronic component that is used in collections of data which enables it to
easy be searched and sorted in various ways.
A spreadsheet is a software
applications that allow users to organize analyze and manipulate data. A good example is a Microsoft spreadsheet
Types of statistics
Statistics has two type’s
namely descriptive statistics and inferential statistics.
Descriptive
statistics
Is a method in statistics
which is used to summarize and describe the important data. Therefore this
statistic is used to provide a general understanding of the data to identify
patterns and to explore relationships between variables. The following are some common descriptive
statistics include:
Measures
of central tendency: Measures of central tendency such as the
mean median and mode are used to describe the typical value in a data. For
example when you want to calculate the mean of the exams scores by finding the
answer of mean score you can get a sense of the overall performance of the
students.
Frequency
distributions: Frequency distributions are used to show
how often different values occur in a data. For example when the hospital have data of eye colors they might create a
frequency distribution to see how many people have brown eyes blue eyes green
eyes etc.
Inferential
statistics
Inferential statistic is
used to make inferences about a population based on a sample of data. This
statistic is used to test hypotheses make predictions and estimate parameters
of the population. The following are some common inferential statistics
include:
Confidence
intervals: is used to
estimate the range of values within which a population is likely to fall with a
certain level of confidence.
Regression
analysis: is a method
in statistics which is used to estimate the relationship between two or more
variables. For example if you want to determine the relationship between a
person’s income and their level of
educations what you must do is to
collect data of income someone
have and their education level and then use regression analysis to estimate the
relationship between these variables.
Analysis
of variance also known has (ANOVA): is a method in statistics which is used in
the testing for differences between two or more groups. For example in a situation that you want to test the
difference in the mean scored for the exams between students who took an online
course and students who took physical classes, you have to collect data on
those scores for both groups and then use ANOVA to test the significant
difference.
Terminologies
Used In Statistics
Data:
Data is any collection of facts, figures, or other information that can be
analyzed.
Variable:
is refers to any characteristic that can
have different values in the data collected.
Population:
is the entire group of individual’s
objects or measurements that the researcher is interested in studying.
Sample: can be said to be a part of the population
that is used to make interpretations about the entire population.
Parameter:
is referred to as a characteristic of a population that is measured or
estimated.
Statistic:
is referred to as a characteristic of a sample that is measured or estimated.
Descriptive
statistics: Descriptive statistic is a method used to describe
or summarize an amount of data.
Inferential
statistics: Inferential statistics is a methods used to make interpret
about a population based on a sample.
Mean:
is referred to as the average of numbers
and it is calculated by adding all the values and dividing by the number of
values.
Median:
is known as the number which is on the middle.
Mode:
is known as the most common value in a set of numbers.
Range:
is referred to as the difference between the largest and the smallest number in
the given data.
Standard
deviation: is known has
a measure of how much the values in an amount of data vary from the mean.
Hypothesis:
is referred to as a statement and also a prediction that is tested using data.
Null
hypothesis: is referred to as a statement that is used to
communicate that there is no significant difference between two groups or
variables.
Alternative
hypothesis: is referred to as a statement that is used to
communicate that there is a significant difference between two groups or
variables.
Confidence
interval: is referred to as a range of values within which a
population parameter is estimated to lie with a certain level of confidence.
P-value:
is referred to as a measure of strength
in the evidence against the null hypothesis.
Correlation: is referred to the measure of strength and
direction of the relationship between two variables.
Regression:
is known as a method used to estimate the relationship between two variables.
Outliers:
Outliers are extreme values in a dataset that are significantly different from
other values.
Skewness:
is referred to as the measure of the irregularity of a distribution in the
given data.
Kurtosis:
is referred to as a measure of the evenness of a distribution.
Normal
distribution: is known has a type of distribution that
is symmetric and bell-shaped.
Standard
error: is referred to as a measure of inconsistency of a
sample statistic such as the mean compared to the population parameter.
Confidence
level: is referred
to as the level of certainty that a population parameter falls within a given
confidence interval.
Margin
of error: is known has
the maximum amount of error expected in a sample statistic due to sampling
variability.
Significance
level: is known as
the likelihood of rejecting the null hypothesis when it is actually true.
Type
I error: happens when null
hypothesis is rejected ad it happens it is actually true.
Type
II error: occurs when the null hypothesis is not rejected when
it is actually false.
T-test:
is a method in statistics that is used
to compare the means of two groups.
ANOVA: is a method in statistics that is used to
compare the means of three or more groups.
Scatter
plot: is a vital method in statistics because it
is a graphical representation of the relationship between two variables.
Confidence
interval: is referred
to as a range of values within which a population parameter is estimated to lie
with a certain level of confidence.
In conclusion statistics
is an important subject for students because it helps learner to understand and
make sense of data they may encounter in their lives. Understanding the basics
of data collection analysis interpretation presentation and organization
students can develop critical thinking and problem solving skills that is
essential in today's data-driven world.
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