Biology20
1/26/23 (First lab)
Science: meaning way of knowing
Scientific method:
1. Observation
2. Questions
3. Hypotheses: an educated guess or essentially a tentative explanation of your equation. If there
is only one hypothesis it can often restrict the research for evidence. Not all statements of
explanation are “good” hypotheses. To be a “good” hypothesis it must be testable and
falsifiable. Scientists do not prove a hypothesis to be true. Instead, data is collected can only
support or not support the hypothesis. Scientists never declare definitively that an explanation is
“proven.”
Hypotheses are stated in two parts: null hypothesis which states that there is no effect (or
difference) of the experimental treatment; and an alternative hypothesis, which states that the
treatment did have some effect.
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Example: a null hypothesis could be: listening to classical music while studying for Bio
20 exams will not have an effect on exam scores.
Example: An alternative hypothesis could be: listening to classical music while studying
for Bio 20 exams will increase exam scores.
4. Prediction: All scientists will make predictions of what the outcome of their questions may be
prior to conducting the experiment. This allows the scientist to design the experiment to test a
specific hypothesis. The prediction will often include the If....then statements and may state the
results they expect if the hypothesis is supported.
Example: If Biology 20 students' study while listening to classical music, then they should do
well on exams.
5. Experimentation: Portion of the scientific method where scientists will test their hypotheses
by carefully designing an experiment. Need large sample size and enough replications to ensure
that trends/differences are not simply due to sampling errors.
* Experimental (controlled) study: researchers introduce a treatment to a randomly
selected subset of study group and study the effects. In such studies there is an experimental
group (or multiple groups) and a control group, which receives no treatment. In any good
experimental design, all the features of the study will be identical with the exception of the
treatment being tested. In other words: a scientific test in which all conditions are kept constant
except for the variable be tested.
*Observational study: researchers record or observe the observations or measurements
without manipulating any variables. Observational studies show that there may be a relationship
but not necessarily a cause-and-effect relationship.
*Data: Best to design an experiment where data that you obtain/collect are quantitative
(measurable or countable) as opposed to qualitative (descriptive).
*Quantitative data: can be continuous, discrete, or categorical. Quantitative experiment
will have at least one independent variable (AKA predictor variable) X-axis and at least one
dependent variable (AKA outcome variable) Y-axis.
Continuous data are measured and can take on any fractional value, i.e. height, speed,
time or temperature.
Discrete data are not divisible, i.e. number of doors on a car (2, 4, 5).
Categorical data placed into groups, i.e. gender or political party affiliation, or education
level.
6. Analysis: Once the experiment has been completed the data needs to be analyzed to determine
if there are any patterns in your data. May types of statistical analysis that can be utilized to
analyze the data. The correct statistical test that is used is important otherwise incorrect statistical
tests could lead to a wrong conclusion.
7. Conclusion: The results of the statistical analysis provides a means by which the scientist can
accept or reject the null hypothesis. From that result, conclusions can be drawn whereby the
results of the experiment are considered in a more inclusive context.
Statistics:
Correlation tests:
These tests look for an association between variable checking whether two variables are related.
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Chi Square ( χ2 test)- chi-square test is used to compare two categorical variables.
Calculating the Chi-Square statistic value and comparing it against a critical value from
the Chi-Square distribution allows to assess whether the observed frequency are
significantly different from the expected frequency. Used when there is continuous data.
Use a bar graph.
The hypothesis being tested for chi-square isHo: Variable x and Variable y are independent
Ha: Variable x and Variable y are not independent.
Comparison tests:
These tests look for the difference between the means of variables:Comparison of Means.
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T-tests are used when comparing the means of precisely two groups (e.g. the average
heights of men and women).
PARAMETRIC TESTS
Parametric tests are the ones that can only be run with data that stick with the “three statistical
assumptions” mentioned above. The most common types of parametric tests are divided into
three categories.
 Regression tests:
These tests are used test cause-and-effect relationships, if the change in one or more
continuous variable predicts change in another variable.
What does p-value of 0.05 mean?
P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the
probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05)
means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means
that no effect was observed.
Definitions:
Testability refers to the ability to run an experiment to test a hypothesis or theory
Falsifiability is the capacity for some proposition, statement, theory or hypothesis to be proven
wrong.
Null hypothesis: The statistical hypothesis that states that there will be no differences between
observed and expected data.; no significant between the two variables in the hypothesis; it’s the
hypothesis that the researcher is trying to disprove; defined as “the commonly accepted fact
(such as the sky is blue) and researcher aim to reject or nullify this fact”.
The null hypothesis proposes that no significant difference exists between a set of given
observations.
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Null: Two sample means are equal.
Alternate: Two sample means are not equal.
To reject a null hypothesis, one needs to calculate test statistics, then compare the result
with the critical value. If the test statistic is greater than the critical value, we can reject
the null hypothesis.
Alternative hypothesis: The alternate hypothesis is just an alternative to the null. For
example, if your null is “I'm going to win up to $1,000” then your alternate is “I'm going to win
$1,000 or more.” Basically, you're looking at whether there's enough change (with the alternate
hypothesis) to be able to reject the null hypothesis.
The example below shows the difference between null vs alternative hypotheses:
Alternate Hypothesis: The world is round
Null Hypothesis: The world is not round.
Independent variable: a variable (often denoted by x ) whose variation does not depend on that
of another. The thing you are testing (cause) & also called the “manipulative variable”
abbreviated “IV”. It is the “if” part of the hypothesis. Always on the X-axis.
Dependent variable: A dependent variable is what you measure (effect) in the experiment
and what is affected during the experiment. The dependent variable responds to the
independent variable. It is called dependent because it "depends" on the independent variable.
Abbreviated “DV” also called the responding variable. It is the “then” part of the hypothesis.
Always on the Y-axis.
Quantitative data refers to any information that can be quantified — that is, numbers.
If it can be counted or measured, and given a numerical value, it's quantitative in
nature. Think of it as a measuring stick. Quantitative variables can tell you "how
many," "how much," or "how often."
Qualitative data is descriptive, expressed in terms of language rather than numerical
values. Qualitative data analysis describes information and cannot be measured or
counted. It refers to the words or labels used to describe certain characteristics or
traits. You would turn to qualitative data to answer the "why?" or "how?" Questions.
TYPES OF VARIABLES
It is important to distinguish the difference between the type of variables because this plays a key
role in determining the correct type of statistical test to adopt. There are two main categories:
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QUANTITATIVE: express the amounts of things (e.g. the number of cigarettes in a
pack). The two different types of quantitative variables are:
CONTINOUS (a.k.a Ratio): is used to describe measures and can usually be divided
into units smaller than one (e.g. 1.50 kg).
DISCRETE (a.k.a Interval): is used to describe counts and usually can’t be divided into
units smaller than one (e.g. 1 cigarette).
CATEGORICAL: express groupings of things (e.g. the different type of fruits). The
three different types of categorical variables are:
ORDINAL: represent data with an order (e.g. rankings).
NOMINAL: represent group names (e.g. brands or species names).
BINARY: represent data with a yes/no or 1/0 outcome (e.g. LEFT or RIGHT).