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    How do you know when to use either Spearman rank, standard error or chi squared when you collect your results?
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    If this is AQA ISA related then it tells you it on the formula sheet... if not then no idea, sorry :/
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    (Original post by Simran Mars Foster)
    How do you know when to use either Spearman rank, standard error or chi squared when you collect your results?
    These are all statistical terms, but really quite different. Basically:

    Spearmen's rank is a correlation coefficient used to determine whether two variables are related. You would use this when you two variables e.g. if you are measuring species diversity of invertebrates at different stream velocities or the patient response to a drug at various dosages.

    When you sample, you are trying to estimate the true population mean from the sample means. Standard error is simply the standard deviation of these sample means. E.g. if you are trying to measure the plant species diversity for a given field and say you sample 10 quadrats - the calculated species diversity from each quadrat is the sample mean for that quadrat. This means we can estimate the true species diversity for the field and the standard deviation of the true species diversity - which is the standard error. In summary, use this whenever you are calculating the mean from different samples and state the SE alongside the mean.

    The Chi-squared Goodness of fit test is a hypothesis test used to determine whether or not the observed results from an experiment match the expected results. E.g. if we breed Aa individuals with Aa we would expect 25% of the offspring to be AA, 50% to be Aa and 25% to be aa. If we then actually get say 23%, 47% and 30% the chi-squared test will assess whether this difference is significant or the genetic model is a good fit. In short, use this when comparing observed frequencies with expected frequencies.
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    (Original post by Muppet Science)
    If this is AQA ISA related then it tells you it on the formula sheet... if not then no idea, sorry :/

    (Original post by Asklepios)
    These are all statistical terms, but really quite different. Basically:

    Spearmen's rank is a correlation coefficient used to determine whether two variables are related. You would use this when you two variables e.g. if you are measuring species diversity of invertebrates at different stream velocities or the patient response to a drug at various dosages.

    When you sample, you are trying to estimate the true population mean from the sample means. Standard error is simply the standard deviation of these sample means. E.g. if you are trying to measure the plant species diversity for a given field and say you sample 10 quadrats - the calculated species diversity from each quadrat is the sample mean for that quadrat. This means we can estimate the true species diversity for the field and the standard deviation of the true species diversity - which is the standard error. In summary, use this whenever you are calculating the mean from different samples and state the SE alongside the mean.

    The Chi-squared Goodness of fit test is a hypothesis test used to determine whether or not the observed results from an experiment match the expected results. E.g. if we breed Aa individuals with Aa we would expect 25% of the offspring to be AA, 50% to be Aa and 25% to be aa. If we then actually get say 23%, 47% and 30% the chi-squared test will assess whether this difference is significant or the genetic model is a good fit. In short, use this when comparing observed frequencies with expected frequencies.
    Thank you both
 
 
 
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