Introduction to Biostatistics
Summary
TLDRهذا النص يقدم مقدمة لعلم الاحصائيات الحيوية، يشرح أهمية الاحصائيات في الحياة اليومية وعلم البيولوجيا، ويسلط الضوء على الفرق بين الإحصاء الوصفي والإحصاء ال演绎. يتضمن النص خطوات لتحليل البيانات البيولوجية مثل تحديد السؤال البيولوجية، صياغة الفرضية، تحديد المتغيرات، اختيار الاختبار الإحصائي، إجراء التجربة، تحليل البيانات، وتقديم النتائج. كما يوصح ببعض المصادر التعليمية للتعلم.
Takeaways
- 📊統計学は生物学や公衆衛生、その他の健康科学分野でデータを適切に解釈するため重要な分野です。
- 🔢 統計情報は日常生活に広く存在し、人口調査や社会メディアの統計など多种形式で使われています。
- 📚 統計学は、数量情報の収集とそのデータの処理方法を指し、大規模な集団に関する推測を小さなサンプルに基づいて引き出す方法も含みます。
- 👨🔬 生物統計학は、生物学、公衆衛生、その他の健康科学分野で生成された科学データの適切な解釈を担当する統計学の分野です。
- 📈 統計学の2つの主要なドメインは記述統計学と推論統計学で、前者は集団から収集したデータを用いてその集団について記述または結論を引き出すのに使用されます。
- 🔎 推論統計学は、サンプルデータを用いてサンプルが取られた总体に関する結論に達します。
- 📊 統計学は、数量データの組織、要約、記述の方法と、それらに基づく推論と一般化の方法に関連しています。
- 👨🎓 統計学のリテラシーは、科学文献を読解、理解、評価するために不可欠です。
- 🔬 自分の研究を実行し、権威的に結果を提示するために生物学者には統計学的リテラシーが必要です。
- 📈 統計学のステップバイステップアプローチは、実験計画、実験実施、データ分析、結果提示の4つの主要なステップから成ります。
- 📚 統計学の初心者向けの優れた書籍やオンラインリソース、統計計算パッケージが提案されています。
Q & A
ما هو البيان البياني؟
-البيان البياني هو فرع من العلوم التطبيقية يتعامل مع تفسير البيانات العلمية المنشأة في مجالات مختلفة مثل البيولوجيا والصحة العامة.
لماذا من المهم دراسة البيان البيئي؟
-يتطلب البيان البيئي المعرفة المطلوبة لقراءة وفهم وتقييم الأدب العلمية، بالإضافة إلى أنه يساعد في إجراء الأبحاث بطريقة علمية وتقديم النتائج بطريقة سليمة.
ما هي الفرق بين الإحصاء الوصفي والإستنتاجي؟
-الإحصاء الوصفي يستخدم البيانات الجمع لتوصيف نفس المجموعة، بينما الإحصاء الاستنتاجي يستخدم بيانات العينة لاستنتاجات حول المجموعة الكبيرة.
ما هي خطوات تحليل البيانات البيولوجية بطريقة مفصلة؟
-يتضمن تحليل البيانات البيولوجية خطوات مثل تحديد السؤال البيئي، تخطيط التجربة، إجراء التجربة، تحليل البيانات، وتقديم النتائج.
ما هي الدورة التعليمية المطلوبة لفهم البيان البيئي؟
-يجب أن تتضمن الدورة التعليمية المعرفة الأساسية في البيان والاستنتاجات، وكيفية تطبيقها في تحليل البيانات العلمية.
كيف يمكن للطلاب التكنولوجيا المساعدة في تحليل البيانات العلمية؟
-يمكن للطلاب استخدام الحزم الإلكترونية الحرة للقيام بعمليات التحليل الاحصائي في أبحاثهم، مثل Staff Pages.org و Social Statistics.
ما هي الكتب المقترحة للتعلم البياني؟
-يتضمن الكتب المقترحة لتعلم البيان البياني كتاب "Fowler and Cohen"، وكتاب "McDonald's Handbook of Biological Statistics".
كيف يمكن للباحثون التأكد من أن البيانات تلبي متطلبات الاختبار الاحصائي؟
-يمكن للباحثون التحقق من أن البيانات تلبي متطلبات الاختبار الاحصائي من خلال تحليل البيانات قبل تطبيق الاختبار، واختيار الاختبار المناسب إذا لم تلبي متطلبات الاختبار المختار.
ما هي أهمية ترتيب الفرضية الصفرية والفرضية البديلة في الأبحاث العلمية؟
-تحدد الفرضية الصفرية والفرضية البديلة النتائج المتوقعة وتساعد في التحقق من الفرضيات المختلفة في الأبحاث العلمية.
Outlines
📊 مقدمة في biostatistics
النص المقدم يتحدث عن الأهمية الأساسية للbiostatistics أو الإحصاء الحيوية في مجال العلوم الحيوية والصحيةة. يبدأ النص بتعريف الbiostatistics، وكيف أن الإحصاء هي جزء من الحياة اليومية للعالميين، وخاصة في الدراسات البيولوجية. يتضمن النص مثالات على الاستخدامات اليومية للإحصاء مثل التقارير السنوية، التعدادات، الدراسات التوزيعية، السجلات الثقافية، والإحصاءات الاجتماعية. يشدد النص على أن الbiostatistics هي جزء من المعرفة المطلوبة للفهم والتقييم للأبحاث العلمية، وأن الbiostatistics تتضمن جمع المعلومات الكمية وطرق التعامل مع هذه المعلومات، بالإضافة إلى تقنيات ال演绎 للمعلومات الكمية.
🔍 تحليل الbiostatistics
يتحدث النص الثانوي عن الbiostatistics وكيفية تنظيم وتلخيص ووصف البيانات القابلة للقياس، وكذلك الأساليب لرسم ال演绎ات عنها. يوضح النص على أن هناك قسمين رئيسيين في الإحصاء: الإحصاء الوصفي والإحصاء ال演绎ي. يصف الإحصاء الوصفي بكيفية استخدام البيانات الجمعة على مجموعة لوصف أو استنتاج النتائج عن نفس المجموعة، مثل ترتيب العلامات الدراسية. أما الإحصاء ال演绎ي فيمكن استخدام البيانات العينة لستنتاج نتائج حول المجموعة الأصلية، مثل مقارنة الطول المتوسط للطلبة في الجامعة. يشدد النص على أهمية الbiostatistics في مجال العلوم الحيوية والصحيةة.
📚 الأهمية المطلوب لفهم biostatistics
يناقش النص ال üçüncü السببين الرئيسيين وراء الحاجة إلى biostatistics أو المعرفة الإحصائية لدى عالمي العلوم الحيوية. الأول هو القدرة على قراءة وفهم وتقييم الأدب العلمية، حيث أن الbiostatistics تساعد على فهم البيانات العلمية وتحليلها. الثاني هو القدرة على إجراء الأبحاث الخاصة وتقديم النتائج بطريقة سليمة، حيث أن الbiostatistics تتطلب من الباحثين التخطيط مسبق للطرق الإحصائية المطلوبة قبل إجراء الدراسة. يشدد النص على أن الbiostatistics جزء من خطة الدراسة وليس بعدة الدراسة.
👨🔬 دور الستاتيستicians في الbiostatistics
يتحدث النص الرابع عن دور الستاتيستicians في الbiostatistics، الذي يتضمن توجيه تصميم التجارب أو الدراسات قبل جمع البيانات، وتحليل البيانات باستخدام الإجراءات الإحصائية الصحيحة، وتقديم النتائج المتكاملة للباحثين والقرارة. يشدد النص على أهمية التخطيط الجيد والتفكير مسبقاً في الbiostatistics قبل إجراء الدراسة، وكيفية تقسيم الbiostatistics إلى خطوات مفصلة لتحليل البيانات البيولوجية.
🔬 خطوات الbiostatistics في تحليل البيانات
يصف النص الخامس خطوات الbiostatistics في تحليل البيانات البيولوجية بشكل مفصل. تبدأ الخطوات بتحديد السؤال البيئي الذي يريد الإجابة عنه، ووضع السؤال في شكل فرضية صفرية وبديلة، وتحديد المتغيرات الهامة، واختيار الاختبار الإحصائي المناسب، وإجراء التجربة، وتحليل البيانات، وعرض النتائج. يشدد النص على أهمية اختيار الاختبار الإحصائي المناسب بناءً على نوع البيانات والمتغيرات، وكيفية تغيير الاختبار إذا احتج الإجراء.
📈 تجميع النتائج والعرض عنها
يتحدث النص السادس في النهاية عن كيفية تجميع النتائج وعرضها بشكل فعال، باستخدام الرسوم البيانية والجداول. يشدد النص على أن الbiostatistics تساعد في تنظيم المعلومات وعرضها بطريقة مناسبة. يلخص النص الخطوات الهامة للبحث البيئي، بدءاً من تحديد السؤال البيئي، وتخطيط التجربة، وإجراء التجربة، وتحليل النتائج، وعرض النتائج.
Mindmap
Keywords
💡biostatistics
💡statistics
💡descriptive statistics
💡inferential statistics
💡null hypothesis
💡alternate hypothesis
💡quantitative information
💡data collection
💡statistical test
💡research design
Highlights
Introduction to biostatistics and its importance in daily life and scientific research.
Definition of statistics as a part of life for biologists and its application in various fields.
Examples of statistical information in daily life such as annual reports, censuses, and social media statistics.
The role of statistics in making informed decisions based on customer feedback and prevalence reports.
Clarification that statistics is not alien or purely mathematical, but already in use in daily life.
Statistics defined as the collection of quantitative information and methods of handling data.
Explanation of how statistics is used to draw inferences about large groups from smaller observations.
The two main parts of statistics: descriptive and inferential statistics.
Descriptive statistics focuses on organizing and summarizing data from a specific group.
Inferential statistics uses sample data to make conclusions about a larger population.
Biostatistics defined as the branch of statistics for interpreting scientific data in biology and health sciences.
The necessity of statistical literacy for reading, understanding, and evaluating scientific literature.
The importance of statistical literacy for biologists to undertake investigations and present results authoritatively.
The role of statisticians in guiding the design of experiments and analyzing data.
A step-by-step approach to analyzing biological data, emphasizing the planning and execution of research.
The process of specifying a biological question, formulating hypotheses, defining variables, and choosing statistical tests.
The flexibility to modify statistical tests based on data collected during experiments.
The final steps of applying statistical tests, integrating results, and effectively communicating them.
Learning resources suggested for the course, including books and online statistical calculation packages.
Transcripts
okay our first chapter is
introduction to biostatistics so in this
chapter we are going to
see that what is biostatistics what is
statistics
and uh why it is important for you to
study this
course
so let's start from the definition of
statistics
and statistic is not something alien and
statistics is part of life of every
serious
biologist and not just biologists it is
part of everyday
life so on everyday basis we have
statistical information around
us in the form of annual reports
various censuses distribution surveys
museum records
or social media statistics so these are
the examples
in which we are going to use some sort
of statistical information in our daily
life
and will report about certain uh data
about certain
records about certain companies for
example
censuses the population censuses for
example
distribution surveys so make you make
use of information from
uh different distribution surveys then
museum records they're also a part of
statistical
information and then the social media
statistics like
what are the user ratings for a specific
app
or uh what is the popularity of certain
type of post or everything so this is
all statistical information that you
have in your
daily life so it is impossible to
imagine life without some sort of
statistical information being readily at
hand for example if you are going to
purchase something you are obviously
going to look for a survey that whether
this
uh what is the feedback of the customers
that have already used that product
and uh or what is the user rating for
that product and where this product is
available
and all of these information and
even when you want to see that if there
is
a disease which is prevalent in our
society or no you're going to
see different surveys or the reports of
the prevalence reports or the incidence
reports
so in daily life you're going to use
some sort of statistical information so
statistics is not something alien and
there is no need to be scared of because
i know the biology
students they're always scared of
statistics that it is something alien or
it is something mathematical purely
and why we are studying this subject so
no this is
that you are using statistics already in
your daily life
so the word statistics it is used in two
senses
it refers to collection of quantitative
information
and methods of handling that sort of
data for example animal reports and
censuses
so this is one way of the use of word
statistics
that it refers to collection of
quantitative information right so
quantitative information means the
information which can be presented in
numbers in quantities
right so this is important that in
statistics you're going to make use of
the information which is quantitative
so it refers to collection of
quantitative information that you are
collecting the information
and as well as the methods of handling
that sort of that data that you have
just collected the information
and how you're going to handle that data
how you're going to arrange
that data how you're going to describe
that data
so for example admin reports and
censuses so in annual reports also and
censuses also you are actually
collecting
the quantitative information and then
you're handling that you are arranging
that information in the form of tables
or graphs or
percentages so this is statistics
another way to use statistics is uh to
draw
inferences about large groups on the
basis of observations made on smaller
ones right so you have
for example a very large population and
you want to have some information about
that population so how you're going to
do that are you going to collect
quantitative information from each and
every member of the population
or are you going to collect information
from a smaller number which is a sample
and then you are going to make
inferences from that sample and then
generalizing those inferences on the
larger group
for example estimating disease
prevalence rates
so um every time you would have heard
that every 10 person in pakistan is
suffering from diabetes or every fifth
person is suffering from cardiovascular
disease and so on
so has anyone actually contacted every
one of you
so i think most of you have never been
contacted for
those surveys you have never been asked
that whether you're suffering from
cardiovascular diseases you are
suffering from diabetes you are
suffering from
hepatitis or no but still that data is
present for your population
in which you are included so how this
data is coming
so it happens that this information is
collected from a smaller group
and then that is generalized over the
entire population
so this is also statistics so usually
we can say that word statistics it not
just includes the collection of
quantitative information
but it also refers to drawing inferences
from that quantitative information
so statistics then is to do with ways of
organizing
summarizing and describing quantifiable
data
and methods of drawing inferences and
generalizing upon them
so we can see that there there are two
aspects of this the first one is that it
deals with ways of organizing
summarizing and describing quantifiable
data so we have already discussed that
data should be quantifiable
that we need data we need information
which is in form of quantities or
numbers
so statistics deals with ways of
organizing summarizing and describing
this quantifiable data
and this part of the statistics is known
as descriptive statistics in which
you're actually describing
that quantitative information that you
have collected
then in the definition you can also see
that there is another part
which is methods of drawing inferences
and generalizing
upon them so what is this method for
right so in this case what you're doing
is that you're collecting quantitative
information
and you are drawing inferences from that
quantitative information
and then you are generalizing that
information those inferences upon a
larger group
and this part of the statistics is known
as inferential statistics
right so these are two main parts
descriptive statistics and inferential
statistics so these are two main domains
of statistics
so descriptive statistic statistics is
using data gathered on a group to
describe or reach conclusions about that
same group only
so this is our descriptive statistics so
this is another way of
defining these two domains of statistics
that
descriptive statistics is that in which
you are using data which is gathered on
a group to describe or reach to
conclusions about
that same group only so you collect
information
from one group and then you
arrange that information and you draw
certain inferences from that information
and you apply them only
only onto the same group so this is
descriptive statistics
for example you collect information from
students
of a class right and you
collect information about their grades
and what you do is you just collect that
information
and you arrange that information
according to the grades that how many
students
scored a plus how many students scored a
how many
b or b plus and c and so on so you
collected that information
and you applied that information only
onto that group
that this class the bs6 semester if this
class is
having uh 50 percent students that
have b or c grades and there are 20
person students that have eight grades
and there are 10 percent
or 20 percent students that have a plus
grades so this is how
that you're gathering information from
one group and you're
using that information for that same
group so you're not going to generalize
that information about entire campus
upon entire university
right so this is descriptive statistics
in which you are describing the
information only for that
group and the other part of this the the
other domain of the statistics which is
inferential statistics
is that you use sample data to reach
conclusions about the population from
which the sample was
taken so here we have a group which is a
sample we collect information from that
group
and we apply that information onto the
larger group onto the population from
which that group was
taken so again we can take the example
of university students
that we want to know the average height
of students
and let's suppose there are 2000
students in the university so is it
possible to contact each and every one
of them and collect the information
about their height or measure their
height
no so it is going to be very difficult
so what we do is that
we collect information from let's say
200 students we measure their heights
and we
collect that we randomly select those
students and take their heights and
we record that data so now we are going
to take the data
and we are going to generalize the data
onto the entire campus
so in this way we are taking information
from a smaller
sub group of the larger group and then
we are applying that information onto
the entire group so this is the
difference between descriptive and
inferential
statistics
so now what is biostatistics so we have
studied the definition of statistics
that and the two main domains of
statistics so now is the question that
what is
biostatistics so biostatistics is the
branch of statistics
which is responsible for the proper
interpretation of scientific
data generated in different fields and
what fields
the field of biology public health and
other health sciences that is biomedical
sciences right
so all the data which is
used in these sciences all the research
which is done in these sciences
and analyzed through statistical
procedures so that comes under the
umbrella of
biostatistics
so why there is a need to of
biostatistics so why you are studying
this subject
right so why is statistics necessary why
do you need to have statistical literacy
there are two main reasons for this the
first one is
that statistical literacy is necessary
to read
understand and evaluate the scientific
literature because you're a biology
student and you're studying books you
are studying scientific books
so you need to have a statistical
literacy in order to understand that
information for example you
you encounter a statement like the
probability that first year bird
will be found in the north sea is
significantly greater than for an
older one chi-square 4.2 degree of
freedom 1
probability less than 0.05
so how you are going to evaluate this
statement unless and until you know that
what is meant by this
chi-square value what is meant by its
degree of freedom
and what is meant by the p value which
is the probability value
so this statement is that probability
that a first year bird
will be found in the north sea is
significantly greater than for an older
one so in this case what others want to
say is
that they have evaluated or they have
surveyed
the population of birds that is found in
the north sea
and they were interested in finding out
that whether the first year words
are common or the older ones are common
so they found out
that the first-year words are more
common as compared to the older ones and
this is how they have described their
statements
so this is a simple statement so you
cannot understand or evaluate
this statement and unless and until you
can see that what kind of the test
is used and what kind of information is
given so here you can see
that there is chi square and guys care
is used for the analysis of frequencies
so it means that they have reached to
this conclusion
based on the comparison of frequencies
so what they did is they counted the
number of the first year birds and they
counted the number of the older birds
and then they compared their frequencies
and by that comparison they came to know
that
uh their frequencies are different and
the first year words are more
frequent as compared to the older
words so you can understand these kind
of statements in your literature only if
you have basic statistical
literacy so it requires statistical
understanding to evaluate such type of
claims which you encounter in your
scientific
literature so this is why the
statistical literacy is important
that it is important for you to read
understand and evaluate the scientific
literature so this is the first reason
that you need to have some
statistical literacy
the second reason is that statistical
literacy is important to biologists
to undertake an investigation on their
own account and to present their results
in an authoritative
form so if you are a biologist and you
are doing your own research
so in the previous example we saw that
when you're studying the scientific
literature you're studying others
researches and you're trying to
understand those researches
but when you are going to undertake your
own research you are going to perform
your own investigation
so again you're going to need
statistical literacy
so a research program must be planned by
anticipating the statistical methods to
be employed so this is the next concept
that
whenever you're going to do a research
program
so it must be planned by anticipating
the statistical methods to be
employed so this is why it is necessary
for you when you are going to carry out
your own
research so you must know that what kind
of the statistical methods you are going
to
employ you're going to apply on your
research
and using the statistical treatment as
an afterthought to make the results
acceptable is not appropriate so
whenever you're going to
present your results to the scientific
community into the scientific papers or
scientific conferences
so they're going to ask you that how you
are going to prove
your results what is the um
validity what is the statistical
authority of these claims
so you have to do the statistical
analysis every time you're going to
present your research or you're going to
present your results
so for that purpose you need to have
statistical literacy and
um you need to have um
knowledge about statistical literacy
well well before
uh applying your research right so
uh if whenever you're planning your
research you must
know of the green statistical technique
that you are going to employ
otherwise if you do the experiment you
collect your results and then you think
okay so now i have the data now i have
the results and
what kind of test can i do no that is
not an appropriate you can do that but
that
is not an appropriate way of doing these
scientific
experiments so whenever you are going to
plan an experiment
you have to um
you have to think of or you have to
mention the statistical approach that
you are going to use the statistical
test or the statistical tool
you are going to use for the analysis of
your
results so what is the role of
statisticians
so role of statisticians is to guide the
design of an experiment or survey prior
to data collection again we are talking
about
free thought that statistics is part of
the planning of experimenters not
an afterthought so statisticians they
are going to
guide biologists if they don't have
complete statistical literacy so they
are going to guide them
about the design of an experimental
survey prior to data collection
and to analyze data using proper
statistical procedures and techniques
and to present an integrated results to
researchers and other decision
makers so these are the role of
statisticians
so they design they they provide a
guidance for the designing of an
experiment
and then when the experiment has been
done our survey has been completed
then they analyze that data they can
analyze those results
and then they can present and interpret
the results to those researchers and to
the decision makers that what is the
interpretation of these
results so these are the rules of
statisticians
now we move on to a step-by-step
analysis of biological data that how we
can
we can adopt an approach which is
scientifically
visible and scientifically valid to do
the research
so a systematic step-by-step approach is
best way to analyze biological data so
you are into the sixth semester and in
the next year you're going to do your
own research
so you must have information that how
you're going to
design your experiment and how you're
going to do your
research so it is always step by step
you cannot start anything at random or
haphazardly
you have to organize yourself you have
to organize a research
plan and then you have to follow those
steps
so the statistical analysis of
biological problem it may be broken down
into following steps number one
to specify the biological question to be
answered
so first of all you should be clear that
what kind of the research are you going
to do
what is your biological question that
you're going to
answer so you must be clear about that
biological
question that you're going to answer in
your research so you have to specify
that biological question
then you have to put the question in the
form of a biological null hypothesis and
alternate hypothesis
right so you are going to you have a
biological question and you are going to
explain
it and you are going to find out the
answer for that biological question
and for that purpose the scientific
method as you must have stated in your
previous classes
is that for an exp for as
for doing the research for addressing a
biological question you have to make
hypothesis
hypothesis is an assumption for that
question a likely answer for that
question so you make an hypothesis and
you test that hypothesis that whether
that hypothesis is true or
no so you have to put your question in
the form of a biological null hypothesis
and alternate hypothesis
and then you have to put your question
in the form of a statistical null
hypothesis and alternate hypothesis
because once you are done with testing
your biological hypothesis
you want to see that whether your
biological hypothesis should be accepted
or not accepted and for that purpose
you're going to need statistics
so you're going to buy statistical tools
and for that purpose you need to
formulate
the statistical null hypothesis and
alternate
hypothesis
so now the fourth step is to determine
which variables are relevant to the
question
so in the first step you have specified
the biological question then in the
second step you have specified
the biological hypothesis and in the
third step you have specified the
statistical hypothesis
right so in the fourth step you are
going to define the variables which you
can
use for the collection of your
information so that you can address your
biological
question so let's suppose your
biological question is that
whether smoking increases the chances of
lung cancer or
no so let's say this is your biological
question
now to answer that question you have to
make biological null hypothesis an
alternate hypothesis
so we are going to make the biological
hypothesis in a way
for example you can say that smoking
increases the chances of lung cancer or
smoking increases the susceptibility of
lung cancer
and in the statistical hypothesis you
can have your statistical null and
alternate hypothesis in the statistical
null hypothesis you can say
that the prevalence of lung cancer is
same
in smokers and non-smokers and
the alternate hypothesis could be that
the prevalence of lung cancer is
different among smokers and
non-smokers so this is how you're going
to state your hypothesis
then you have to determine that the
variables which you can measure
to check your hypothesis that whether
your hypothesis is true or
no so for that purpose what kind of
variable are you going to use
you are obviously going to look for the
smoking history of the
individuals suffering from lung cancer
individuals are not suffering from the
lung cancer
so in that case you are going to be
individuals and you are going to ask
them questions about
their smoking history and then
you can ask them about their
smoking behavior that whether they're
smokers or non-smokers
or they're ex-smokers and if they're
current smokers then
how many cigarettes a day they smoke so
so you make these variable
and then you collect information about
those variables from the individuals
so this is how you define the variable
so once you have information about their
smoking history only then you can test
your hypothesis
so this is what is meant by defining the
variables that what kind of information
you are going to collect from those
individuals right so
the next step is to determine what kind
of variable each one is that whether you
have a quantitative variable or you have
a quantitative variable
and because we are talking about
statistics so we need to have
quantitative information we have you
need to have quantitative
variable which can be presented in
numbers so again
in that case you're going to see that
whether you have the
um ordinal variable or you have a
continuous variable or you have a
discontinuous variable
so you don't worry about this definition
we are going to check these definitions
in
the coming chapters so right now i'm
just
going to describe you that what is meant
by the kind of variables so there could
be different kinds of variables
there could be variables which are
measured on a discontinuous scale there
are variables which could be measured on
a continuous scale
there could be variables which are the
measurement of something there could be
variables which are the counting of
something
there could be uh variables which are
measured on
an ordinal scale on a ratio scale or on
an interval scale
so we are going to define that what type
of variable is this one
then in the sixth step uh based on the
number of variables the kind of
variables and the hypothesis to be
tested then you have to choose the best
statistical test
to use there are a number of statistical
tests that are available but not
everyone is suitable for
the kind of variable that you have the
kind of data you have
so you have to find out that which kind
of statistical test is going to be good
for your data for example we were
talking about the
relationship between smoking history and
the prevalence of lung cancer
so for that kind of test we can um
for that kind of analysis we can choose
the chi-square test of independence and
we can just check that
what is the differences in their
frequencies so again it depends on the
kind of the
variables and the kind of your problem
and the kind of your hypothesis that
what kind of statistical test you are
going to
choose now do the experiment now you see
that this is the time to do the
experiment so before that you have to
plan all these things you have to plan
your question you have to plan
you have to make or design specific
hypothesis
and then you have to define that which
variables you are going to collect your
during your data collection or doing
your experiments
and then you also have to define that
what kind of the statistical tests you
are going to employ
and only then you're going to do the
experiment right
so that was all pre-planning and
statistics was the part of that planning
of your experiment
so now you do the experiment
and now you have done the experiment or
you have collected your
observations or informations now you
have to examine the data to see if it
needs the assumptions of the statistical
test you
chose if it doesn't you should choose a
more appropriate test
for example uh you are studying the
relationship between smoking and
the susceptibility of lung cancer
right so you define your variable that
you are going to
take the smoking history from the
individuals who are suffering from lung
cancer and the religious were not
suffering from the lung cancer and then
you are going to compare them
through the guys care test of
independence right
but when you were doing uh the data
collection when you were
surveying those individuals you came to
know that
there is another factor that can
contribute to that
susceptibility and that factor is let's
say age
that the lung cancer is more common in
older individuals as compared to the
younger individuals
so in that case we're going to add
another variable
which could be a contributing factor in
susceptibility as
well right so in that case what you're
going to do is
that if your uh the statistical that
test that you chose
whether that test is allowing you the
comparison of multiple
variables or is it not allowing you the
comparison of multiple variables so if
it does then you can use the same test
and if it doesn't you can choose a more
appropriate
test right so it is flexible
that you have to include the information
about statistical tests into your
planning
but if due to some unavoidable reasons
if
due to some um other unforcing reasons
you have to add or remove the variables
then you have to modify your statistical
test as
well and you can do that
now you can apply your chosen
statistical test and then you can
integrate your
results and then once you have
reached your results then it is the time
to communicate your results effectively
and usually with a graph or table and
again statistics is going to help you
and that that how you are going to
organize so remember the definition
statistics
that it deals with the ways of
collecting organizing
and describing the quantitative
information
so statistics helps you in collecting
the information
and organizing that information and it
also helps you in the
representation of that information that
how you're going to present that
information
what kind of the tables are suitable
what kind of the graphs are suitable for
presenting your
data so a quick review of
the step-by-step approach to the
analysis of biological data
so that you have a biological question
that you want to answer
you plan an experiment and the planning
of experiment involves uh formulating
the biological hypothesis formulating
the statistical hypothesis
defining the variables choosing the
statistical tests
and then you do the experiment so after
planning you do the experiment and you
get your
data you get your information so what is
the next step you're going to analyze
that data
and again you're going to make use of
statistics for analyzing your data and
getting your results
and then the next step is to present
your results so this is the summary of
um of the steps that you can adopt
to carry out your research that you have
to first identify your biological
question
then you have to plan your experiment
then you do the experiment
and you analyze your results and then
you present your results
right so this is how you do it so this
is the summary of
the research design or the research
planning
that you can employ
and now the learning resources for your
course
so um i'm going to suggest a couple of
books
the first one is fowler and cohen this
is a very good book and very excellent
book for the beginners who don't have
background of statistics as of as
discipline or as a subject just like the
biologist so we don't have a background
of
statistics so this book is very much
relevant for us
so this is a guide for practical
statistics for field biology
and it addresses the basic questions and
it gives the
examples from biology so most of the
content of this course that i am going
to discuss with you comes from this book
so this is a very good book and then
there is a book which is available
online
and you can download the pdf or from the
link which is given here
and this book is by mcdonald and this is
handbook of
biological statistics so again this is a
very basic book
and very easy to understand and you can
just download
the pdf version of this book from this
link
and then there are some online
statistical calculation packages that
you can use they're free to use
and you can use them for statistical
analysis in your research when you're
going to do research in your next
semester
staff pages.org social statistics
statistics.com or vesserstats.net
right so these are some of the online
statistical calculation packages that
you can
use
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