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Biology A Level OCR > Module 1 - Development and Practical Skills > Flashcards

Module 1 - Development and Practical Skills Flashcards

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1
Q

A good experiment gives results that are…(4 key things):

A
  1. Precise/Reliable - results don’t vary much from the mean. Precision is reduced by random error.
  2. Repeatable and Reproducible - repeatable (if same method + equipment = same results) reproducible (if different person, with slightly diff method/ piece of equipment results = still the same)
  3. Valid - valid results answer the original question. To get valid results you need to control all the variables.
  4. Accurate - accurate results are really close to the true answer. Human interpretation can reduce the accuracy.
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2
Q

What 4 things should you consider when designing a good experiment?

A
  1. Only ONE variable should be changed:
    usually change one variable and measure its effect on another variable
    E.g: light intensity is independent variable and rate of photosynthesis is dependent variable.
  2. All the other variables should be CONTROLLED + CONSTANT:
    Means you can be sure that only your independent variable is affecting the thing you’re measuring
    E.g: ph, temperature, time
  3. Use NEGATIVE CONTROLS :
    used to check that only the independent variable is affecting the dependent variable. Aren’t expected to have any effect on the experiment.
    E.g: having the experiment carried out in the dark should also be used, as no photosynthesis should happen with this control
  4. Repeat at least 3 times + calculate a MEAN:
    reduces effect of random error, makes results more precise. If repeats get similar results each time, shows that your data is repeatable and makes it more likely to be reproducible.
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3
Q

What is the difference between an independent variable and dependent variable?

A

Independent variable = the variable that you change

whereas,

Dependent variable = the variable that you measure

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4
Q

What do you need to decide when planning an experiment?

A

What you’re going to measure
how often you’re going to take measurements (intervals)
the appropriate/suitable apparatus, equipment and techniques

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5
Q

Most important part of choosing apparatus, equipment and techniques?

A

That it is the most appropriate for the function it needs to perform

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6
Q

How to obtain precise results?

A

By using the apparatus and techniques correctly

Make sure measuring things using appropriate units + record them properly as any calculations you do will be affected and your conclusions may be wrong

Make sure to perform all the techniques carefully and that any apparatus is set up correctly to help minimize errors

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7
Q

What are useful about graduated pipettes?

A

They have a scale so you can measure specific volumes

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8
Q

How to correctly use a pipette?

A

Read the meniscus at eye level - read the volume at the bottom of the meniscus

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9
Q

What is the meniscus of a pipette?

A

The curved upper surface of the liquid inside the pipette

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10
Q

How to use water baths correctly?

A

Allow time for water baths to heat up before starting

The solutions need time to get to the same temperature as the water before you start the experiment

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11
Q

What helps you work safely?

A

Carrying out a risk assessment when planning an experiment

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12
Q

3 things to identify in a risk assessment

A
  1. all the dangers, e.g: hazardous chemicals, microorganisms or naked flames
  2. Who is at risk
  3. What can be done to reduce the risk, e.g: wearing goggles/gloves, hair tied back, fume cupboard.
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13
Q

Other than dangers, what other thing do you need to consider in your experiment?

A

Ethical issues - if using living animals (e.g insects) you must treat them with respect:

  • handling them carefully
  • keeping them away from harmful chemicals, extreme heat sources and things that might cause them physical discomfort
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14
Q

What to record your results in?

A

A record table

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15
Q

What do repeats help spot?

A

Anomalous results

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16
Q

What does processing the data help you to do?

A

Interpret it

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17
Q

What is the equation for percentage change?

A

Percentage change = (final value - original value) ÷ original value x 100

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18
Q

What do percentage change results show?

A

Positive value = a percentage increase

Negative value = a percentage decrease

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19
Q

What does calculating percentage change help to do?

A

Helps to quantify how much something has changed

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20
Q

What do averages and range do with your data?

A

Summarise them

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21
Q

How to work out mean?

A

When you’ve done repeats of an experiment. You should always calculate the mean:

Add together all the data values and divide by the total number of values in the sample

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22
Q

What is the median?

A

The middle number when you put all your data in numerical order

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23
Q

What is the mode?

A

The number that appears most often in a set of data

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24
Q

What is the range, and how do you calculate it?

A

How spread out the data is

Find the largest data value and subtract the smallest data value from it

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25
Q

What can be more useful than the range and why?

A

Standard Deviation

Because it tells you how values are spread about the mean rather than just the total spread of data

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26
Q

What does standard deviation results show?

A

A small standard deviation means the repeated results are all close to the mean - so the results are all similar and precise

27
Q

What are statistical tests used for?

A

To analyse data

28
Q

What are two other examples of statistical tests other than standard deviation?

A

The Student’s t-test

The Chi-squared test

29
Q

When can you use Student’s t-test and what does it show?

A
  • when you have two sets of data that you want to compare
  • It tests whether there is a significant difference in the means of two data sets
  • The value obtained is compared to a critical value, which helps you decide how likely it is that the results or ‘differences in the means’ were due to chance
30
Q

What does a student’s t test result that is greater than the critical value (5%) show and what is it called?

A

That you can be 95% confident that the difference is significant and not due to chance.

Called a 95% confidence limit - good enough to reject the null hypothesis

31
Q

What is the null hypothesis?

A

A special type of hypothesis used with statistical tests.

It states that there is no significant difference between the things you’re measuring

32
Q

When can you use Chi-Squared?

A

When you have categorical (grouped) data and you want to compare whether your observed results are statistically different from your expected results.

33
Q

What does a chi-squared result that is greater than the critical value (P=0.05), show?

A

That you can be 95% certain the difference is significant

34
Q

What is standard form?

A

When processing data that has very big or very small numbers that have lots of zeros and changing it into something more manageable

35
Q

How to work out standard form?

A

By moving the decimal point left or right.

The number of places the decimal point moves is then represented by a power of 10 - positive for big numbers, negative for numbers smaller than one.

E.g: 16500 = 1.65 x 10 (power of 4)

E.g: 0.000362 = 3.62 x 10 (power of -4)

36
Q

What is the main benefit of presenting data in graphs or chart?

A

Makes results much easier to interpret

37
Q

What is qualitative data?

A

non-numerical data e.g. blood group

38
Q

What is discrete data?

A

Numerical data that can only take up certain values in a range e.g. shoe size

39
Q

What presentation should you use for qualitative and discrete data?

A

Bar charts or Pie charts

40
Q

What is continuous data?

A

Data that can take any value in a range, e.g. height or weight

41
Q

What presentation should you use for continuous data?

A

Histograms or Line graphs

42
Q

When you want to plot one variable against another what presentation should you use?

A

Scatter Graph

  • positive correlation
  • negative correlation
  • no correlation
43
Q

Why should you use logarithms?

A

Because it’s tricky to plot graphs with very small and very large and very large numbers (e.g. 0.1 and 1000) on the same axis.

So to make it easier, convert the values to their logarithms and plotting them on a logarithmic scale (e.g. a log10 scale)

44
Q

What is a log10 scale?

A

A scale where each value is ten times larger than the value before. This means the numbers 1, 2, 3 and 4 on a log10 scale represent 10, 100, 1000 and 10 000 on a linear (normal scale)

45
Q

How to calculate logarithms?

A

You need to use the log button on your calculator. On most calculators ‘log’ will stand for log10, but different calculators work differently, so make sure you know how to use yours.

46
Q

What is rate?

A

Rate is a measure of how much something is changing over time.

Useful when analysing your data E.g finding the rate of a reaction

47
Q

How to work out rate from a graph?

A

For a linear graph you can calculate the rate by finding the gradient of the line:

Gradient = Change in Y ÷ Change in X

(X to the side, Y to the sky)

48
Q

What is the equation of a straight line, and how is it useful?

A

The equation of a straight line can always be written as y = mx + c
where m = gradient
and c = y-intercept (the value of y when the line crosses the y-axis)

Knowing the equation of the line allows you to estimate results not plotted on the graph.

49
Q

How is a non-linear graph different to a straight line graph?

A

It is a curved graph

50
Q

How to find the rate of a non-linear graph?

A

By drawing a tangent:

  1. Position a ruler on the graph at the point where you want to know the rate.
  2. Angle the ruler so there is equal space between the ruler and the curve on either side of the point
  3. Draw a line along the ruler to make the tangent
  4. Extend the line right across the graph - it’ll help to make your gradient calculation easier as you’ll have more points to choose from.
  5. Calculate the gradient of the tangent to find the rate.
51
Q

What do conclusions need to be?

A

Valid

A conclusion can only be considered as valid if it answers the original question

52
Q

What do the relationship (correlation) between two variables often conclude?

A

Positive correlation = as one variable increases the other increases

Negative correlation = as one variable increases the other decreases

No correlation = there is no relationship between the variables

53
Q

What does the statistical test - Spearman’s rank correlation coefficient allow you to do?

A

It allows you to work out the degree to which two sets of data are correlated.
It is given as a value between 1 and -1:

A value of 1 = strong positive correlation

0 = no correlation

-1 = strong negative correlation

54
Q

Problem with Spearman’s Rank Correlation Coefficient

A

You have to be careful when drawing conclusions from data like this because a correlation between two variables doesn’t always mean that a change in one variable causes a change in the other.

the correlation could be due to:

  • chance
  • or a third variable having effect
55
Q

Define Causal Relationship

A

When there’s a relationship between two variables and a change in one variable does cause a change in the other.

56
Q

How can it be concluded that a correlation is a causal relationship

A

Only if every other variable that could possibly affect the result is controlled.

  • very hard to do as correlations are generally accepted to be causal relationships if lots of studies have found the same thing, and scientists have figured out exactly how one factor causes the other.
57
Q

When making a conclusion do you use generalisations or specificity?

A

BE SPECIFIC

You can only conclude what the results show and NO more.

58
Q

Define uncertainty in conclusions

A

The amount of error your measurements might have:

The results you get from an experiment won’t be completely perfect - there’ll always be a degree of uncertainty due to limits in the sensitivity of the apparatus you’re using

59
Q

What does the +- sign tell and what is it called?

A

Tells you the range in which the true value lies.

This range is called the margin of error

60
Q

How to calculate the percentage error of your measurements

A

If you know the uncertainty value of your measurements, you can calculate the percentage error:

Percentage error = (uncertainty ÷ reading) x 100

61
Q

How to minimise the errors in the measurements?

A

Obvious way is to buy the most sensitive equipment available. However, not most realistic

SO

You can plan your experiment so you measure a greater amount of something to reduce your percentage error.

62
Q

What do you need to sum up your experiment and data in an evaluation?

A
  1. Assess your experiment and data through:
    - Repeatability - Did you take enough repeat readings/measurements? Would you do more repeats if you were to do the experiment again? Do you think you’d get similar data if you did the experiment again?
    - Reproducibility: Have you compared your results with other people’s results? Were your results similar? Could other scientists gain data showing the same relationships that are shown in your data?
    - Validity: Does your data answer the question you set out to investigate?
63
Q

What do you need to evaluate your method?

A

Is there anything you could have done to make your results more precis/accurate?
Were there any limitations?
Were there sources of error in your experiment?
Could you have used more sensitive apparatus/equipment?
How could you refine and improve?

64
Q

What can your evaluation help?

A

Help you decide how much confidence you have ; high degree or low degree.

Biology A Level OCR (2 decks)

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