Introduction to Biostatistics

Summary

هذا 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

00:00

📊 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.

05:01

🔍 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.

10:03

📚 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.

15:03

👨‍🔬 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.

20:04

🔬 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.

25:04

📈 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.

Keywords

💡biostatistics

Biostatistics هو فرع من الإحصاء يستخدم في العلوم الحيوية والصحية، ويتعلق بتفسير البيانات العلمية بشكل صحيح. في الفيديو، يستخدم المصطلح لشرح الأهمية الأساسية للاستاتستيكا في تحليل البيانات العلمية، مثل الأبحاث البيولوجية والصحيةة، وكيفية استخدام البيانات الكمية لفهم النماذج واتخاذ القرارات العلمية.

💡statistics

الإحصاء هو علم يستخدم لجمع وترتيب ووصف البيانات الكمية، واستخراج ال演绎ات من البيانات. في الفيديو، يستخدم للإشارة إلى دور الأرقام والبيانات في فهم العالم واتخاذ القرارات، مثل التقرير السنوي والتعدادات والدراسات التوزيعية.

💡descriptive statistics

الإحصاء الوصفي هو فرع من الإحصاء يستخدم لوصف البيانات الكمية بطريقة تسمح بفهمها وتحليلها. في الفيديو، يستخدم للإشارة إلى الطريقة التي يتم بها ترتيب البيانات وبيانها لوصف مجموعة معينة، مثل ترتيب العلامات الدراسية للطلاب.

💡inferential statistics

الإحصاء ال演绎ي هو فرع من الإحصاء يستخدم لاستخراج ال演绎ات عن مجموعة كبيرة بناءً على الملاحظات على مجموعة أصغر. في الفيديو، يستخدم للإشارة إلى الطريقة التي يتم بها جمع البيانات من عينة وتطبيقها على المجموعة الكبيرة، مثل تقدير معدل انتشار الأمراض.

💡null hypothesis

الفرض ال_NULL__ هو فرضية تثبت أنها لا توجد فرق بين المجموعات في الدراسة. في الفيديو، يستخدم للإشارة إلى الطريقة العلمية التقليدية لوضع假设 للبحث، حيث يتم اختبار假设 ال_NULL__ قبل الacceptance أو الrejection.

💡alternate hypothesis

الفرض البديل هو فرضية تثبت أنها يوجد فرق بين المجموعات في الدراسة. في الفيديو، يستخدم للإشارة إلى النتيجة المطلوبة إذا اتضح أن假设 ال_NULL__ غير صحيح، مثل وجود فرق في معدل انتشار الأمراض بين الSmokers والnon-smokers.

💡quantitative information

المعلومات الكمية هي المعلومات التي يمكن م量的ها بأرقام. في الفيديو، يستخدم للإشارة إلى النوع المطلوب من المعلومات لإجراء التحليل الإحصائي، مثل التعدادات والمتوسطات.

💡data collection

جمع البيانات هو عملية جمع المعلومات المطلوبة لإجراء التحليل. في الفيديو، يستخدم للإشارة إلى خطوة مهمة في الresearch حيث يتم جمع المعلومات الكمية من الأفراد، مثل سجل التاريخ التدخيني.

💡statistical test

اختبار الإحصاء هو طريقة تستخدم لتحليل البيانات واستخراج ال演绎ات العلمية. في الفيديو، يستخدم للإشارة إلى الطريقة التي يتم بها اختيار اختبار مناسب بناءً على النوع والحجم للبيانات، مثل اختبار chí-square للمقارنة بين المجموعات.

💡research design

تصميم الresearch هو خطة تحدد الطريقة التي سيتم بها إجراء الresearch. في الفيديو، يستخدم للإشارة إلى الخطوات المطلوبة قبل إجراء الresearch، مثل تحديد السؤال البيئي وتحديد ال假设ات والإحصائيات المطلوبة.

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